Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability ...

Graph pattern query is a powerful tool for extracting crucial information from property graphs. With the exponential growth of sizes, property graphs are typically divided into multiple subgraphs (referred to as partitions) and stored across various ...

Detecting locally, non-overlapping, near-clique densest subgraphs is a crucial problem for community search in social networks. As a vertex may be involved in multiple overlapped local cliques, detecting locally densest sub-structures considering h-...

Heterogeneous graphs provide a universal data structure for representing various kinds of structured data in numerous domains. The graph alignment problem aims to find the correspondences of vertices in different graphs, playing a fundamental role in ...

Graph pattern matching is powerful and widely applicable to many application domains. Despite the recent algorithm advances, matching patterns in large-scale real-world graphs still faces the memory access bottleneck on conventional computing systems. ...

Sparse matrices are often used to model the interactions among different objects and they are prevalent in many areas including e-commerce, social network, and biology. As one of the fundamental matrix operations, the sparse matrix chain multiplication (...

The interplay between local consistency and global consistency has been the object of study in several different areas, including probability theory, relational databases, and quantum information. For relational databases, Beeri, Fagin, Maier, and ...

We consider the Distinct Shortest Walks problem. Given two vertices s and t of a graph database D and a regular path query, we want to enumerate all walks of minimal length from s to t that carry a label that conforms to the query. Usual theoretical ...

Local community search (LCS) finds a community in a given graph G local to a set R of seed nodes by optimizing an objective function. The objective function f(S) for an induced subgraph S encodes the set inclusion criteria of R to a classic community ...

A graph parameter is a function f on graphs with the property that, for any pair of isomorphic graphs G₁ and G₂, f(G₁)=f(G₂). The Weisfeiler--Leman (WL) dimension of f is the minimum k such that, if G₁ and G₂ are indistinguishable by the k-dimensional WL-...

k-clique listing is a vital graph mining operator with diverse applications in various networks. The state-of-the-art algorithms all adopt a branch-and-bound (BB) framework with a vertex-oriented branching strategy (called VBBkC), which forms a sub-...

Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based ...

A graph models the connections among objects. One important graph analytical task is clustering which partitions a data graph into clusters with dense innercluster connections. A line of clustering maximizes a function called modularity. Modularity-based ...

Mining cohesive subgraphs from a graph is a fundamental problem in graph data analysis. One notable cohesive structure is γ-quasi-clique (QC), where each vertex connects at least a fraction γ of the other vertices inside. Enumerating maximal γ-quasi-...

As an important cohesive subgraph model in bipartite graphs, the (α, β)-core (a.k.a. bi-core) has found a wide spectrum of real-world applications, such as product recommendation, fraudster detection, and community search. In these applications, the ...

k-defective cliques relax cliques by allowing up-to k missing edges from being a complete graph. This relaxation enables us to find larger near-cliques and has applications in link prediction, cluster detection, social network analysis and transportation ...

Subgraph matching is a basic operation in graph analytics, finding all occurrences of a query graph Q in a data graph G. A common approach is to first filter out non-candidate vertices in G, and then order the vertices in Q to enumerate results. Recent ...

In this paper, for the first time, we introduce the concepts of window-CCs and window-SCCs on undirected and directed temporal graphs, respectively. We then study the queries of window-CC and window-SCC by developing several efficient index-based query ...

Subgraph matching, which finds subgraphs isomorphic to a query, is crucial for information retrieval from data represented as a graph. To avoid redundant explorations in the data, existing methods restrict the search space by extracting candidate ...

Generating data perturbations to graphs has become a useful tool for analyzing the robustness of Graph Neural Networks (GNNs). However, existing model-driven methodologies can be prohibitively expensive to apply in large graphs, which hinders the ...