Attributed Hypergraph Clustering (AHC) aims at partitioning a hypergraph into clusters such that nodes in the same cluster are close to each other with both high connectedness and homogeneous attributes. Existing AHC methods are all based on matrix ...

Recently, queries that find bursting patterns in temporal graph data have received increasing research attention. In particular, finding the flow in temporal networks whose flow values are bursting in a time interval has numerous applications, such as ...

In many real-world applications (e.g., email networks, social networks, and phone call networks), the relationships between entities can be modeled as a temporal graph, in which each edge is associated with a timestamp representing the interaction time. ...

The s-bundle, as a cohesive subgraph model which relaxes the clique, remains connected whenever fewer than n-s vertices are removed, where n is the number of vertices inside. Finding the largest s-bundle is a fundamental problem and has diverse ...

Given a graph pair (G¹, G²), graph edit distance (GED) is defined as the minimum number of edit operations converting G¹ to G². GED is a fundamental operation widely used in many applications, but its exact computation is NP-hard, so the approximation of ...

The ((k,p))-core model was recently proposed to capture engagement dynamics by considering both intra-community interactions (i.e., the k-core structure) and inter-community interactions (i.e., the p-fraction property). It is a refinement of the classic ...

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-...