Machine learning for subgraph extraction: Methods, applications and challenges
Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the
associated original graphs, and many graph properties are known to be inherited by …
associated original graphs, and many graph properties are known to be inherited by …
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 …
Learned sketch for subgraph counting: a holistic approach
Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …
subgraphs in a data graph that match a given query graph by either homomorphism or …
Computing graph edit distance via neural graph matching
Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory.
Given a graph pair (G 1, G 2), GED is defined as the minimum number of primitive …
Given a graph pair (G 1, G 2), GED is defined as the minimum number of primitive …
Neural Attributed Community Search at Billion Scale
Community search has been extensively studied in the past decades. In recent years, there
is a growing interest in attributed community search that aims to identify a community based …
is a growing interest in attributed community search that aims to identify a community based …
Graphmm: Graph-based vehicular map matching by leveraging trajectory and road correlations
Map matching of sparse vehicle trajectories is a fundamental problem in location-based
services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on …
services, such as traffic flow analysis and vehicle routing. Existing literature mainly relies on …
gsword: Gpu-accelerated sampling for subgraph counting
Subgraph counting is a fundamental component for many downstream applications such as
graph representation learning and query optimization. Since obtaining the exact count is …
graph representation learning and query optimization. Since obtaining the exact count is …
Fast Local Subgraph Counting
We study local subgraph counting queries, Q=(p, o), to count how many times a given k-
node pattern graph p appears around every node υ in a data graph G when the given center …
node pattern graph p appears around every node υ in a data graph G when the given center …
Prerequisite-driven Fair Clustering on Heterogeneous Information Networks
This paper studies the problem of fair clustering on heterogeneous information networks
(HINs) by considering constraints on structural and sensitive attributes. We propose a …
(HINs) by considering constraints on structural and sensitive attributes. We propose a …
Inductive Attributed Community Search: To Learn Communities Across Graphs
Attributed community search (ACS) aims to identify subgraphs satisfying both structure
cohesiveness and attribute homogeneity in attributed graphs, for a given query that contains …
cohesiveness and attribute homogeneity in attributed graphs, for a given query that contains …