Search Results for author:
Found 107 papers, 44 papers with code
Heterogeneous Graph Transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.
Ranked #25 on
Node Property Prediction
on ogbn-mag
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
REVEAL consists of four key components: the memory, the encoder, the retriever and the generator.
Ranked #9 on
Visual Question Answering (VQA)
on OK-VQA
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs.
Ranked #1 on
Graph Similarity
on IMDb
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
Convolutional Set Matching for Graph Similarity
We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Throughout our analysis, we connect our findings to previously-proposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities.
Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general.
Ranked #3 on
Node Classification
on AMZ Computers
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding
However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.
Differentiable Product Quantization for End-to-End Embedding Compression
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Few-Shot Representation Learning for Out-Of-Vocabulary Words
Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
In this work, we propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.
Ranked #1 on
Graph Classification
on RE-M12K
Embedding Uncertain Knowledge Graphs
However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge.
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.
Ranked #1 on
Graph Classification
on Web
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.
Ranked #3 on
Knowledge Graph Completion
on DPB-5L (French)
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encoding of the discrete symbols.
Towards Fine-Grained Reasoning for Fake News Detection
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.
Coupled Graph ODE for Learning Interacting System Dynamics
On one hand, features of objects change over time, influenced by the linked objects in the interaction graph.
Neural Compositional Rule Learning for Knowledge Graph Reasoning
NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head.
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model.
Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings.
Ranked #2 on
Knowledge Graph Completion
on DPB-5L (French)
Code Recommendation for Open Source Software Developers
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Explaining machine learning models is an important and increasingly popular area of research interest.
Bi-Level Attention Graph Neural Networks
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs).
Hierarchical Attention Models for Multi-Relational Graphs
BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2) relation-level attention.
Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
Deck building is a crucial component in playing Collectible Card Games (CCGs).
Learning Fair Representations via an Adversarial Framework
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval.
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species.
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs
For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures.
Dissimilar Nodes Improve Graph Active Learning
The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks.
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
We further bridge GCN's preferential attachment bias with unfairness in link prediction and propose a new within-group fairness metric.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Fast Inference of Removal-Based Node Influence
We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node.
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations
Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols.
On Sampling Strategies for Neural Network-based Collaborative Filtering
In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.
Joint Text Embedding for Personalized Content-based Recommendation
While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items.
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero.
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.
GLSearch: Maximum Common Subgraph Detection via Learning to Search
However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on heuristic search algorithms which in practice cannot find good solution for large graph pairs given a limited computation budget.
Software Language Comprehension using a Program-Derived Semantics Graph
To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI).
Ideology Detection for Twitter Users with Heterogeneous Types of Links
The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors.
Social and Information Networks
Differentiable Product Quantization for Learning Compact Embedding Layers
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Decoupled Greedy Learning of Graph Neural Networks
We empirically analyse our proposed DGL-GNN model, and demonstrate its effectiveness and superior efficiency through a range of experiments.
Learning to Search for Fast Maximum Common Subgraph Detection
Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc.
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.
Unsupervised Adversarially-Robust Representation Learning on Graphs
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.
Motif-Driven Contrastive Learning of Graph Representations
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
CREATe: Clinical Report Extraction and Annotation Technology
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
Clinical Named Entity Recognition using Contextualized Token Representations
To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.
Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task.
Relation-Guided Pre-Training for Open-Domain Question Answering
Answering complex open-domain questions requires understanding the latent relations between involving entities.
A Primer on Deep Learning for Causal Inference
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.
UniKER: A Unified Framework for Combining Embedding and Definite Horn Rule Reasoning for Knowledge Graph Inference
Knowledge graph inference has been studied extensively due to its wide applications.
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level.
NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.
Learning to Transfer via Modelling Multi-level Task Dependency
Besides, the understanding of relationships among tasks has been ignored by most of the current methods.
Learning Compact Embedding Layers via Differentiable Product Quantization
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Demystifying Graph Neural Network Via Graph Filter Assessment
However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.
Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction
Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair.
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks
Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging.
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
Question-Answer Sentence Graph for Joint Modeling Answer Selection
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems.
Improving Multi-Task Generalization via Regularizing Spurious Correlation
First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.
Schema-Guided Event Graph Completion
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network.
Detecting Political Biases of Named Entities and Hashtags on Twitter
We examine a variety of applications and we thereby demonstrate the effectiveness of our PEM model.
Learning Probabilities of Causation from Finite Population Data
This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data.
Unit Selection: Learning Benefit Function from Finite Population Data
In this paper, we present a machine learning framework that uses the bounds of the benefit function that are estimable from the finite population data to learn the bounds of the benefit function for each cell of characteristics.
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Answering open-domain questions requires world knowledge about in-context entities.
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+10
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
In this paper, we move towards combining large parametric models with non-parametric prototypical networks.
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Hence, those models are unable to predict further future.
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
In addition, these programs can be compiled and converted into a control data flow graph (CDFG), and the compiler also provides fine-grained alignment between the code tokens and the CDFG nodes.
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units.
Meta-Path-based Probabilistic Soft Logic for Drug-Target Interaction Prediction
Drug-target interaction (DTI) prediction, which aims at predicting whether a drug will be bounded to a target, have received wide attention recently, with the goal to automate and accelerate the costly process of drug design.
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity.
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges.
Weisfeiler and Leman Go Measurement Modeling: Probing the Validity of the WL Test
The expressive power of graph neural networks is usually measured by comparing how many pairs of graphs or nodes an architecture can possibly distinguish as non-isomorphic to those distinguishable by the $k$-dimensional Weisfeiler-Leman ($k$-WL) test.
Unveiling Invariances via Neural Network Pruning
Invariance describes transformations that do not alter data's underlying semantics.
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
Breakdown analysis further highlights RESPROMPT particularly excels in complex multi-step reasoning: for questions demanding at least five reasoning steps, RESPROMPT outperforms the best CoT based benchmarks by a remarkable average improvement of 21. 1% on LLaMA-65B and 14. 3% on LLaMA2-70B.
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.
Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics
In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
We demonstrate in our experiments that SymGNN can significantly improve the energy barrier prediction over other GNNs and non-graph machine learning models.
An Evaluation of Large Language Models in Bioinformatics Research
Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities.
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text
Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time.
