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Found 64 papers, 27 papers with code
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged.
Relationship Discovery for Drug Recommendation
Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
Dual-Granularity Medication Recommendation Based on Causal Inference
Medication recommendation aims to integrate patients' long-term health records with medical knowledge, recommending accuracy and safe medication combinations for specific conditions.
Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph
By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links.
Cycle Invariant Positional Encoding for Graph Representation Learning
To efficiently encode the space of all cycles, we start with a cycle basis (i. e., a minimal set of cycles generating the cycle space) which we compute via the kernel of the 1-dimensional Hodge Laplacian of the input graph.
AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection
Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention.
CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code
Automatically generating function summaries for binaries is an extremely valuable but challenging task, since it involves translating the execution behavior and semantics of the low-level language (assembly code) into human-readable natural language.
StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation
After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data.
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction.
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
In this paper, we propose a fine-grained Token-level retrieval-augmented mechanism (Tram) on the decoder side rather than the encoder side to enhance the performance of neural models and produce more low-frequency tokens in generating summaries.
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models.
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data.
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description
In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula.
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #25 on
Relation Extraction
on DocRED
A Study of the Attention Abnormality in Trojaned BERTs
Trojan attacks raise serious security concerns.
When Does A Spectral Graph Neural Network Fail in Node Classification?
Although graph filters provide theoretical foundations for model explanations, it is unclear when a spectral GNN will fail.
Neural Approximation of Graph Topological Features
Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods.
Cycle Representation Learning for Inductive Relation Prediction
In this paper, we consider rules as cycles and show that the space of cycles has a unique structure based on the mathematics of algebraic topology.
Graph Representation Learning
Inductive Relation Prediction
+1
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction
It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all nodes in the graph.
Federated Inference through Aligning Local Representations and Learning a Consensus Graph
On the top is a federation of the local data representations, performing global inference that incorporates all distributed parts collectively.
A Topological View of Rule Learning in Knowledge Graphs
We propose to collect cycle bases that span the space of cycles.
Graph Information Matters: Understanding Graph Filters from Interaction Probability
We show that the homophily degree of graphs significantly affects the prediction error of graph filters.
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Sampling is an established technique to scale graph neural networks to large graphs.
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks.
Link Prediction with Persistent Homology: An Interactive View
Link prediction is an important learning task for graph-structured data.
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
In this paper, we propose an innovative node representation learning framework, Wasserstein Graph Neural Network (WGNN), to mitigate the problem.
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Graph convolutional networks (GCNs) have achieved great success on graph-structured data.
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach
We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology.
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks.
Deep Graph Matching and Searching for Semantic Code Retrieval
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs.
Pre-Training BERT on Domain Resources for Short Answer Grading
Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data.
Graph Enhanced Cross-Domain Text-to-SQL Generation
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
We propose the graph energy neural network (GENN) to explicitly model link type correlations.
Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.
DeepDrawing: A Deep Learning Approach to Graph Drawing
Node-link diagrams are widely used to facilitate network explorations.
Pre-training of Graph Augmented Transformers for Medication Recommendation
G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities.
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Ranked #2 on
Graph Classification
on IPC-lifted
CGNF: Conditional Graph Neural Fields
By integrating the conditional random fields (CRF) in the graph convolutional networks, we explicitly model a joint probability of the entire set of node labels, thus taking advantage of neighborhood label information in the node label prediction task.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #4 on
Dynamic Link Prediction
on DBLP Temporal
Reaching Data Confidentiality and Model Accountability on the CalTrain
We also demonstrate that when malicious training participants tend to implant backdoors during model training, CALTRAIN can accurately and precisely discover the poisoned and mislabeled training data that lead to the runtime mispredictions.
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI.
AWE: Asymmetric Word Embedding for Textual Entailment
It refers to the directional relation between text fragments such that the "premise" can infer "hypothesis".
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
We focus on the matrix representation of graphs and formulate penalty terms that regularize the output distribution of the decoder to encourage the satisfaction of validity constraints.
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related environments with different data qualities.
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning.
Ranked #3 on
Node Classification
on Citeseer Full-supervised
Multilingual Training of Crosslingual Word Embeddings
Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer.
Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data
In this paper, we try to address two challenges of applying topic models to lexicon extraction in non-parallel data: 1) hard to model the word relationship and 2) noisy seed dictionary.
Learning Crosslingual Word Embeddings without Bilingual Corpora
Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools.
Bilingual Lexicon Induction
Cross-Lingual Document Classification
+4
