Neural subgraph counting with Wasserstein estimator
Subgraph counting is a fundamental graph analysis task which has been widely used in
many applications. As the problem of subgraph counting is NP-complete and hence …
many applications. As the problem of subgraph counting is NP-complete and hence …
Current and future directions in network biology
Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …
sciences, is critical for deepening understanding of cellular functioning and disease. While …
Reinforcement learning based query vertex ordering model for subgraph matching
Subgraph matching is a fundamental problem in various fields that use graph structured
data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query …
data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query …
Polarity-based graph neural network for sign prediction in signed bipartite graphs
As a fundamental data structure, graphs are ubiquitous in various applications. Among all
types of graphs, signed bipartite graphs contain complex structures with positive and …
types of graphs, signed bipartite graphs contain complex structures with positive and …
Neural similarity search on supergraph containment
Supergraph search is a fundamental graph query processing problem. Supergraph search
aims to find all data graphs contained in a given query graph based on the subgraph …
aims to find all data graphs contained in a given query graph based on the subgraph …
Counterfactual inference graph network for disease prediction
B Zhang, X Guo, Q Lin, H Wang, S Xu - Knowledge-Based Systems, 2022 - Elsevier
Graph convolutional networks are widely used as computational models that integrate the
data of image and non-image modalities in the medical diagnostic domain, especially while …
data of image and non-image modalities in the medical diagnostic domain, especially while …
Bipartite graph capsule network
Graphs have been widely adopted in various fields, where many graph models are
developed. Most of previous research focuses on unipartite or homogeneous graph …
developed. Most of previous research focuses on unipartite or homogeneous graph …
Enhancement of traffic forecasting through graph neural network-based information fusion techniques
To improve forecasting accuracy and capture intricate interactions within transportation
networks, information fusion approaches are crucial for traffic predictions based on graph …
networks, information fusion approaches are crucial for traffic predictions based on graph …
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions
The interactions between structured entities play important roles in a wide range of
applications such as chemistry, material science, biology, and medical science. Recently …
applications such as chemistry, material science, biology, and medical science. Recently …
Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding (Technical Report)
The classic problem of exact subgraph matching returns those subgraphs in a large-scale
data graph that are isomorphic to a given query graph, which has gained increasing …
data graph that are isomorphic to a given query graph, which has gained increasing …