Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Existing analyses relate this notion to the graph isomorphism problem, which is most…
Graph ClassificationSize Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective
We investigate size-induced distribution shifts in graphs and assess their impact on the ability of graph neural networks (GNNs) to generalize to larger graphs relative to the training data. Existing literature presents …
Graph ClassificationSequential Graph Neural Networks for Source Code Vulnerability Identification
Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. However, this task is rather challenging owin…
Graph ClassificationGraph Neural NetworkDEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their appl…
Graph ClassificationNode ClassificationTractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by ver…
Graph ClassificationGraph Representation LearningRepresentation LearningDRew: Dynamically Rewired Message Passing with Delay
Message passing neural networks (MPNNs) have been shown to suffer from the phenomenon of over-squashing that causes poor performance for tasks relying on long-range interactions. This can be largely attributed to message…
Graph ClassificationGraph RegressionInductive BiasLink Prediction+1SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce…
Contrastive LearningGraph ClassificationTransfer LearningStrengthening structural baselines for graph classification using Local Topological Profile
We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the …
Graph ClassificationGraph Representation LearningModel SelectionRepresentation LearningDeep Graph Reprogramming
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, without amending raw node features nor model…
3D Object RecognitionAction RecognitionGraph ClassificationObject RecognitionTGNN: A Joint Semi-supervised Framework for Graph-level Classification
This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-l…
Graph ClassificationGraph Neural NetworkID-MixGCL: Identity Mixup for Graph Contrastive Learning
Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/graph representations. The underlying assu…
Contrastive LearningGraph ClassificationNode ClassificationStochastic Subgraph Neighborhood Pooling for Subgraph Classification
Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting …
ClassificationData AugmentationGraph ClassificationGraph Representation Learning+1Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT
In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extens…
Community DetectionGraph ClassificationGraph LearningIn-Context Learning+5Transformer and Snowball Graph Convolution Learning for Brain functional network Classification
Advanced deep learning methods, especially graph neural networks (GNNs), are increasingly expected to learn from brain functional network data and predict brain disorders. In this paper, we proposed a novel Transformer a…
Graph ClassificationTopological Pooling on Graphs
Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a number of approaches adopting a graph poo…
Anomaly DetectionGraph ClassificationGraph LearningGraph Representation Learning+1Structural Imbalance Aware Graph Augmentation Learning
Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes ha…
Graph ClassificationLink PredictionNode ClassificationA Comparison of Graph Neural Networks for Malware Classification
Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant feature…
ClassificationGraph ClassificationGraph Neural NetworkMalware ClassificationExphormer: Sparse Transformers for Graphs
Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs wh…
Graph ClassificationGraph LearningGraph RegressionLink Prediction+1MPool: Motif-Based Graph Pooling
Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification. Current GNN models apply different graph pooling methods that reduce the number of nodes…
ClusteringGraph ClassificationGraph Positional Encoding via Random Feature Propagation
Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding. Surprisingly, however, there is still no clear understanding of the relation…
Graph ClassificationNode Classification