Search Results for author:
Found 177 papers, 99 papers with code
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Answering real-world user queries, such as product search, often requires accurate retrieval of information from semi-structured knowledge bases or databases that involve blend of unstructured (e. g., textual descriptions of products) and structured (e. g., entity relations of products) information.
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data.
Ranked #1 on
Binary Classification
on kickstarter
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i. e., row and column sums).
Representation Learning for Frequent Subgraph Mining
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers.
Compositional Generative Inverse Design
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes that are more complex than those in the training data.
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics.
TimeGraphs: Graph-based Temporal Reasoning
The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks.
Relational Deep Learning: Graph Representation Learning on Relational Databases
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts.
Holistic Evaluation of Text-To-Image Models
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption.
Context-Aware Meta-Learning
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning.
Ranked #1 on
Few-Shot Image Classification
on Mini-Imagenet 5-way (5-shot)
(using extra training data)
In-Context Learning for Few-Shot Molecular Property Prediction
In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction.
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outcome (e. g., improving accuracy).
Large Language Models as Analogical Reasoners
Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process.
Communication-Free Distributed GNN Training with Vertex Cut
Here, we introduce CoFree-GNN, a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training.
Ranked #2 on
Node Classification
on Reddit
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Med-Flamingo: a Multimodal Medical Few-shot Learner
However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
Enabling tabular deep learning when $d \gg n$ with an auxiliary knowledge graph
However, for tabular datasets with extremely high $d$-dimensional features but limited $n$ samples (i. e. $d \gg n$), machine learning models struggle to achieve strong performance due to the risk of overfitting.
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage.
PRODIGY: Enabling In-context Learning Over Graphs
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
Learning Large Graph Property Prediction via Graph Segment Training
Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.
Geometric Latent Diffusion Models for 3D Molecule Generation
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design.
Learning Controllable Adaptive Simulation for Multi-resolution Physics
In this work, we introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions.
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Homophily principle, i. e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks
Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude.
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task.
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction
However, many important molecular properties depend on complex molecular characteristics -- such as the various 3D geometries a molecule may adopt or the types of chemical interactions it can form -- that are not explicitly encoded in the feature space and must be approximated from low amounts of data.
Zero-shot causal learning
There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.
Learning Graph Search Heuristics
At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process.
Retrieval-Augmented Multimodal Language Modeling
To integrate knowledge in a more scalable and modular way, we propose a retrieval-augmented multimodal model, which enables a base multimodal model (generator) to refer to relevant text and images fetched by a retriever from external memory (e. g., documents on the web).
Ranked #7 on
Image Captioning
on MS COCO
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
Traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems.
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Extensive evaluation of TuneUp on five diverse GNN architectures, three types of prediction tasks, and both transductive and inductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes.
Efficient Automatic Machine Learning via Design Graphs
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.
Deep Bidirectional Language-Knowledge Graph Pretraining
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.
Ranked #1 on
Riddle Sense
on RiddleSense
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Our pretrained model can then be directly applied to target few-shot tasks on without the need for training few-shot tasks.
ROLAND: Graph Learning Framework for Dynamic Graphs
Finally, we propose a scalable and efficient training approach for dynamic GNNs via incremental training and meta-learning.
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy.
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way.
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
We test our method in a 1D benchmark of nonlinear PDEs, 2D Navier-Stokes flows into turbulent phase and an inverse optimization of boundary conditions in 2D Navier-Stokes flow.
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
Learning Backward Compatible Embeddings
We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models.
ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems.
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering
Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e. g., the context in question and answer; "QA context") and structured (e. g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge.
MultiBiSage: A Web-Scale Recommendation System Using Multiple Bipartite Graphs at Pinterest
MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings.
PinnerFormer: Sequence Modeling for User Representation at Pinterest
Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years.
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training.
LinkBERT: Pretraining Language Models with Document Links
Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks.
Ranked #1 on
Semantic Similarity
on BIOSSES
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.
Ranked #1 on
Ancestor-descendant prediction
on WN18RR
Ancestor-descendant prediction
Knowledge Graph Completion
+2
GreaseLM: Graph REASoning Enhanced Language Models
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.
Leveraging the Cell Ontology to classify unseen cell types
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution.
Neural Distance Embeddings for Biological Sequences
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.
LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.
Ranked #2 on
Grammatical Error Correction
on Unrestricted
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Combiner: Full Attention Transformer with Sparse Computation Cost
However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.
Ranked #2 on
Language Modelling
on Wiki-40B
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
Ranked #2 on
Riddle Sense
on RiddleSense
Language-Agnostic Representation Learning of Source Code from Structure and Context
Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on
Knowledge Graphs
on WikiKG90M-LSC
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Model-Agnostic Graph Regularization for Few-Shot Learning
In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.
Driver2vec: Driver Identification from Automotive Data
In this paper, we develop a deep learning architecture (Driver2vec) to map a short interval of driving data into an embedding space that represents the driver's behavior to assist in driver identification.
Open-World Semi-Supervised Learning
Here, we introduce a novel open-world semi-supervised learning setting that formalizes the notion that novel classes may appear in the unlabeled test data.
Ranked #2 on
Open-World Semi-Supervised Learning
on ImageNet-100
Identity-aware Graph Neural Networks
However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Temporal networks serve as abstractions of many real-world dynamic systems.
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation
We use ForceNet to perform quantum chemistry simulations, where ForceNet is able to achieve 4x higher success rate than existing ML models.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Coresets for Robust Training of Deep Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets.
Design Space for Graph Neural Networks
However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function.
Coresets for Robust Training of Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequently found in real-world datasets.
Ranked #36 on
Image Classification
on mini WebVision 1.0
F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks.
Handling Missing Data with Graph Representation Learning
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
Graph Information Bottleneck
We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks.
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.
Ranked #5 on
Complex Query Answering
on NELL-995
Multi-hop Attention Graph Neural Network
Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
Inductive Learning on Commonsense Knowledge Graph Completion
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
DE captures the distance between the node set whose representation is to be learned and each node in the graph.
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
We propose a variational inference framework OCEAN to perform online task inference for compositional tasks.
Concept Learners for Few-Shot Learning
Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance.
Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between neurons.
PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
Latent user representations are widely adopted in the tech industry for powering personalized recommender systems.
Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph.
Improving Query Safety at Pinterest
Identifying unsafe queries is necessary to protect users from inappropriate query suggestions.
M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Learning to Simulate Complex Physics with Graph Networks
Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.
Unifying Graph Convolutional Neural Networks and Label Propagation
Both solve the task of node classification but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information.
Ranked #1 on
Node Classification
on Coauthor Phy
Relational Message Passing for Knowledge Graph Completion
Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph.
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Ranked #4 on
Complex Query Answering
on FB15k-237
G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.
Hyperbolic Graph Convolutional Neural Networks
Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs.
Ranked #1 on
Link Prediction
on PPI
(Accuracy metric)
Improving Graph Attention Networks with Large Margin-based Constraints
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.
Coresets for Accelerating Incremental Gradient Methods
But because at each epoch the gradients are computed only on the subset S, we obtain a speedup that is inversely proportional to the size of S. Our subset selection algorithm is fully general and can be applied to most IG methods.
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space.
Selection via Proxy: Efficient Data Selection for Deep Learning
By removing hidden layers from the target model, using smaller architectures, and training for fewer epochs, we create proxies that are an order of magnitude faster to train.
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.
Coresets for Data-efficient Training of Machine Learning Models
Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function.
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on
Molecular Property Prediction
on ToxCast
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
Ranked #1 on
Recommendation Systems
on Dianping-Food
Select Via Proxy: Efficient Data Selection For Training Deep Networks
In our approach, we first train a small proxy model quickly, which we then use to estimate the utility of individual training data points, and then select the most informative ones for training the large target model.
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms.
GNNExplainer: Generating Explanations for Graph Neural Networks
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Learning Dynamic Embeddings from Temporal Interactions
Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions.
Predicting pregnancy using large-scale data from a women's health tracking mobile application
Predicting pregnancy has been a fundamental problem in women's health for more than 50 years.
Complete the Look: Scene-based Complementary Product Recommendation
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
How Powerful are Graph Neural Networks?
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Ranked #1 on
Graph Classification
on COX2
MASA: Motif-Aware State Assignment in Noisy Time Series Data
Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior.
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data.
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
In this Review, we describe the principles of data integration and discuss current methods and available implementations.
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on
Graph Classification
on REDDIT-MULTI-12K
Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data
With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.
Embedding Logical Queries on Knowledge Graphs
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.
Ranked #6 on
Complex Query Answering
on FB15k-237
Dynamic Network Model from Partial Observations
We propose a novel framework for providing a non-parametric dynamic network model--based on a mixture of coupled hierarchical Dirichlet processes-- based on data capturing cascade node infection times.
Network Enhancement: a general method to denoise weighted biological networks
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome.
Prioritizing network communities
Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory.
Community Interaction and Conflict on the Web
Here we study intercommunity interactions across 36, 000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
Modeling polypharmacy side effects with graph convolutional networks
The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.
Ranked #1 on
Link Prediction
on Decagon
Spectral Graph Wavelets for Structural Role Similarity in Networks
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Large-scale analysis of disease pathways in the human interactome
However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations.
Learning Structural Node Embeddings Via Diffusion Wavelets
Nodes residing in different parts of a graph can have similar structural roles within their local network topology.
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems
The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i. e. fleets of self-driving vehicles).
Robotics Multiagent Systems Systems and Control Applications
Representation Learning on Graphs: Methods and Applications
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.
Predicting multicellular function through multi-layer tissue networks
We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues.
Interpretable & Explorable Approximations of Black Box Models
To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences.
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively.
Driver Identification Using Automobile Sensor Data from a Single Turn
In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals.
Inductive Representation Learning on Large Graphs
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Community Identity and User Engagement in a Multi-Community Landscape
To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is.
Higher-order clustering in networks
Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster.
An Army of Me: Sockpuppets in Online Discussion Communities
Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users.
Loyalty in Online Communities
In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time.
Network Inference via the Time-Varying Graphical Lasso
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements.
Motifs in Temporal Networks
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience.
Higher-order organization of complex networks
Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges.
Social and Information Networks Discrete Mathematics Physics and Society
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
We propose Confusions over Time (CoT), a novel generative framework which facilitates a multi-granular analysis of the decision making process.
node2vec: Scalable Feature Learning for Networks
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
A word's sentiment depends on the domain in which it is used.
Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification.
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test.
Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Mental illness is one of the most pressing public health issues of our time.
Growing Wikipedia Across Languages via Recommendation
The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests.
Social and Information Networks Digital Libraries
Do Cascades Recur?
In this paper, we perform a large-scale analysis of cascades on Facebook over significantly longer time scales, and find that a more complex picture emerges, in which many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between.
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Social networking websites allow users to create and share content.
Social and Information Networks Physics and Society Applications 60G55, 62P25 H.2.8
QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns
By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage.
Antisocial Behavior in Online Discussion Communities
User contributions in the form of posts, comments, and votes are essential to the success of online communities.
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion.
How Community Feedback Shapes User Behavior
Interestingly, the authors that receive no feedback are most likely to leave a community.
Can Cascades be Predicted?
On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future.
Engaging with Massive Online Courses
We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations.
The Bursty Dynamics of the Twitter Information Network
Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure.
Nonparametric Multi-group Membership Model for Dynamic Networks
Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time.
A Computational Approach to Politeness with Application to Social Factors
We propose a computational framework for identifying linguistic aspects of politeness.
Modeling Information Propagation with Survival Theory
Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases.
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience.
Structure and Dynamics of Information Pathways in Online Media
We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network.
Learning to Discover Social Circles in Ego Networks
We develop a model for detecting circles that combines network structure as well as user profile information.
Image Labeling on a Network: Using Social-Network Metadata for Image Classification
Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community.
Defining and Evaluating Network Communities based on Ground-truth
Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable gold-standard ground-truth.
Social and Information Networks Physics and Society
On the Convexity of Latent Social Network Inference
Moreover, our approach scales well as it can infer optimal networks on thousands of nodes in a matter of minutes.
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
A large body of work has been devoted to defining and identifying clusters or communities in social and information networks.
Data Structures and Algorithms Data Analysis, Statistics and Probability Physics and Society
Cost-effective Outbreak Detection in Networks
We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude.
Sampling From Large Graphs
Thus graph sampling is essential. The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e. g., the diameter), to get estimates for the large graph.
