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Found 51 papers, 28 papers with code
Predicting Online Video Engagement Using Clickstreams
In the nascent days of e-content delivery, having a superior product was enough to give companies an edge against the competition.
Will This Paper Increase Your h-index? Scientific Impact Prediction
The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare.
Social and Information Networks Digital Libraries Physics and Society H.2.8; H.3.7
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm.
Detecting Anomalies in Sequential Data with Higher-order Networks
A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Social and Information Networks Physics and Society
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements
Conditions are essential in the statements of biological literature.
HONEM: Learning Embedding for Higher Order Networks
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years.
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there have been extensive studies.
Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance.
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e. g., hourly, weekly, and weekday patterns).
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
AttrE2vec: Unsupervised Attributed Edge Representation Learning
Representation learning has overcome the often arduous and manual featurization of networks through (unsupervised) feature learning as it results in embeddings that can apply to a variety of downstream learning tasks.
Few-Shot Graph Learning for Molecular Property Prediction
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.
Ranked #1 on
Molecular Property Prediction (1-shot))
on Tox21
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
An important advantage of DeepSMOTE over GAN-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection.
Graph Barlow Twins: A self-supervised representation learning framework for graphs
The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling.
Predicting Terrorist Attacks in the United States using Localized News Data
From a machine learning perspective, we found that the Random Forest model outperformed several deep models on our multimodal, noisy, and imbalanced data set, thus demonstrating the efficacy of our novel feature representation method in such a context.
Few-Shot Learning on Graphs
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
Learning effective recipe representations is essential in food studies.
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
We then propose RecipeRec, a novel heterogeneous graph learning model for recipe recommendation.
Deep Ensembles for Graphs with Higher-order Dependencies
Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure.
Graph-based Molecular Representation Learning
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
Heterogeneous Graph Masked Autoencoders
In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges.
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs.
Linkless Link Prediction via Relational Distillation
In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i. e., predicted logit-based matching and node representation-based matching.
FakeEdge: Alleviate Dataset Shift in Link Prediction
In this paper, we first identify the dataset shift problem in the link prediction task and provide theoretical analyses on how existing link prediction methods are vulnerable to it.
Interpretable ML for Imbalanced Data
We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance.
Knowledge Distillation on Graphs: A Survey
Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.
Conditional expectation with regularization for missing data imputation
Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance.
Class-Imbalanced Learning on Graphs: A Survey
Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic.
Towards Understanding How Data Augmentation Works with Imbalanced Data
Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood.
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.
Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health
This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability.
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
This discrepancy stems from a fundamental limitation: while MPNNs excel in node-level representation, they stumble with encoding the joint structural features essential to link prediction, like CN.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout
With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
The surge in prompt-based learning within Natural Language Processing (NLP) suggests the potential of adapting a "pre-train, prompt" paradigm to graphs as an alternative.
Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories
The widespread application of machine learning techniques to biomedical data has produced many new insights into disease progression and improving clinical care.
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges.
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction.
TinyLLM: Learning a Small Student from Multiple Large Language Models
To solve the problems and facilitate the learning of compact language models, we propose TinyLLM, a new knowledge distillation paradigm to learn a small student LLM from multiple large teacher LLMs.
Universal Link Predictor By In-Context Learning on Graphs
In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
UGMAE: A Unified Framework for Graph Masked Autoencoders
In light of this, we propose UGMAE, a unified framework for graph masked autoencoders to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency.
Can we Soft Prompt LLMs for Graph Learning Tasks?
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks.
Node Duplication Improves Cold-start Link Prediction
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks.
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
The opioid crisis has been one of the most critical society concerns in the United States.
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains.
You do not have to train Graph Neural Networks at all on text-attributed graphs
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities.
