Papers Subgraph Counting
“Subgraph Counting” 태그가 달린 논문 17편 · 필터 해제
Higher-Order Graph Databases
Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order (one-hop) relations, which are crucial …
Graph LearningSubgraph CountingStudying and Improving Graph Neural Network-based Motif Estimation
Graph Neural Networks (GNNs) are a predominant method for graph representation learning. However, beyond subgraph frequency estimation, their application to network motif significance-profile (SP) prediction remains unde…
Graph GenerationGraph Neural NetworkGraph Representation LearningRepresentation Learning+1BEACON: A Benchmark for Efficient and Accurate Counting of Subgraphs
Subgraph counting the task of determining the number of instances of a query pattern within a large graph lies at the heart of many critical applications, from analyzing financial networks and transportation systems to u…
BenchmarkingSubgraph CountingHomomorphism Expressivity of Spectral Invariant Graph Neural Networks
Graph spectra are an important class of structural features on graphs that have shown promising results in enhancing Graph Neural Networks (GNNs). Despite their widespread practical use, the theoretical understanding of …
Subgraph CountingRevisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements
Graphs are essential data structures for modeling complex interactions in domains such as social networks, molecular structures, and biological systems. Graph-level tasks, which predict properties or classes for the enti…
Contrastive LearningGraph ClassificationMolecular Property PredictionProperty Prediction+1Discovering Motifs to Fingerprint Multi-Layer Networks: a Case Study on the Connectome of C. Elegans
Motif discovery is a powerful and insightful method to quantify network structures and explore their function. As a case study, we present a comprehensive analysis of regulatory motifs in the connectome of the model orga…
Subgraph CountingRepresentation Learning for Frequent Subgraph Mining
Identifying frequent subgraphs, also called network motifs, is crucial in analyzing and predicting properties of real-world networks. However, finding large commonly-occurring motifs remains a challenging problem not onl…
Representation LearningSubgraph CountingBeyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community. So far, GNN expressiveness has been primarily assessed via the Weisfeiler-Lehman (WL) hierarchy. However, such an …
Graph LearningSubgraph CountingCommunication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions
We suggest the use of hash functions to cut down the communication costs when counting subgraphs under edge local differential privacy. While various algorithms exist for computing graph statistics, including the count o…
Data CompressionSubgraph CountingOn the Power of the Weisfeiler-Leman Test for Graph Motif Parameters
Seminal research in the field of graph neural networks (GNNs) has revealed a direct correspondence between the expressive capabilities of GNNs and the $k$-dimensional Weisfeiler-Leman ($k$WL) test, a widely-recognized me…
Subgraph CountingThe Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs …
Subgraph CountingSurveyDeSCo: Towards Generalizable and Scalable Deep Subgraph Counting
We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training. Firstly, DeSCo uses a novel c…
Graph Neural NetworkGraph RegressionPositionSubgraph CountingExpressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that are provably more powerful than traditional Message Passing Neural Networks (MPNNs). In particular, we use adversarial robustness a…
Adversarial RobustnessSubgraph CountingImproving Expressivity of Graph Neural Networks using Localization
In this paper, we propose localized versions of Weisfeiler-Leman (WL) algorithms in an effort to both increase the expressivity, as well as decrease the computational overhead. We focus on the specific problem of subgrap…
Subgraph CountingSubgraph Counting - 2 starSubgraph Counting - 3 StarSubgraph Counting - C4+3Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams
As the popularity of graph data increases, there is a growing need to count the occurrences of subgraph patterns of interest, for a variety of applications. Many graphs are massive in scale and also fully dynamic (with i…
Subgraph CountingEvaluating Graph Generative Models with Contrastively Learned Features
A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the …
Subgraph CountingTwo-level Graph Neural Network
Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level i…
Graph Neural NetworkSubgraph CountingVocal Bursts Valence Prediction