Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.

In many real-world applications such as social network analysis, knowledge graph discovery, biological network analytics, and so on, graph data management has become increasingly important and has drawn much attention from the database community. While many graphs (e.g., Twitter, Wikipedia, etc.) are usually evolving over time, it is of great importance to study the continuous subgraph matching (CSM) problem, a fundamental, yet challenging, graph operator, which continuously monitors subgraph matching results over dynamic graphs with a stream of edge updates. To efficiently tackle the CSM problem, we carefully design a general CSM processing framework, based on novel DynamIc Vertex DomINance Embedding (DIVINE), which maps vertex neighborhoods into an embedding space to enable efficient subgraph matching and incremental maintenance under dynamic updates. Inspired by low pruning power for high-degree vertices, we propose a new degree grouping technique to decompose high-degree star patterns into groups of lower-degree star substructures, and devise degree-aware star substructure synopses (DAS 3 ) over embeddings of star substructure groups. We develop efficient algorithms to incrementally maintain dynamic graphs and answer CSM queries by traversing DAS 3 synopses and applying our designed vertex dominance and range pruning strategies. Through extensive experiments, we confirm the efficiency of our proposed DIVINE approach over both real and synthetic graphs.

For the past decades, the subgraph similarity search over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely subgraph similarity search under aggregated neighbor difference semantics (S 3 AND), which identifies subgraphs g in a data graph G that are similar to a given query graph q by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the S 3 AND problem, we design two effective pruning methods, keyword set and aggregated neighbor difference lower bound pruning , which rule out false alarms of candidate vertices/subgraphs to reduce the S 3 AND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient S 3 AND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our S 3 AND approach over both real and synthetic graphs under various parameter settings.

The Israeli-Palestinian conflict is complex and longstanding. This study uses textual analysis through the lens of moral foundations theory to explore how moral values, emotional expressions, and political ideologies are reflected in tweets about the Israeli-Palestine Conflict. By analyzing public discourse and social media interactions, the study seeks to uncover the underlying moral frameworks and emotional responses that shape public perspectives on this ongoing conflict. The results reveal three key findings: 1) both anti-Israel and anti-Hamas tweets emphasize conflict, war, and human rights concerns, characterized by strong negative emotions; 2) anti-Hamas tweets exhibit higher emotional intensity, particularly around specific violent incidents and key figures; and 3) anti-Israel tweets encompass a broader range of issues, such as campus protests and anti-Semitism, with a focus on Israel’s policies and actions from a moral and human rights perspective. This study combines emotion and moral framework analysis, which is rarely used for war related discourse. By combining these perspectives, the research provides valuable theoretical and practical insights that deepen our understanding of how moral and emotional factors shape public opinion on the Israeli-Palestinian conflict.

In many real-world applications such as social network analysis and online marketing/advertising, community detection is a fundamental task to identify communities (subgraphs) in social networks with high structural cohesiveness. While previous works focus on detecting communities alone, they do not consider the collective influences of users in these communities on other user nodes in social networks. Inspired by this, in this paper, we investigate the influence propagation from some seed communities and their influential effects that result in the influenced communities. We propose a novel problem, named Top-L most Influential Community DEtection ($\text{Top}L$-ICDE) over social networks, which aims to retrieve top-$L$seed communities with the highest influences, having high structural cohesiveness, and containing user-specified query keywords. To efficiently tackle the$\text{Top}L$-ICDE problem, we design effective pruning strategies to filter out false alarms of seed communities and propose an effective index mechanism to facilitate efficient Top-$L$community retrieval. We develop an efficient$\text{Top}L$-ICDE answering algorithm by traversing the index and applying our proposed pruning strategies. We also formulate and tackle a variant of$\text{Top}L$-ICDE, named diversified top-L most influential community detection ($\text{Top}L$-ICDE), which returns a set of$L$diversified communities with the highest diversity score (i.e., collaborative influences by$L$communities). We prove that$\text{DTop}L$-ICDE is NP-hard, and propose an efficient greedy algorithm with our designed diversity score pruning. Through extensive experiments, we verify the efficiency and effectiveness of our proposed$\text{Top}L$-ICDE and$\text{DTop}L$-ICDE approaches over real/synthetic social networks under various parameter settings.

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 importance in many real-world applications such as social network analysis, knowledge graph discovery in the Semantic Web, bibliographical network mining, and so on. In this paper, we propose a novel and effective graph neural network (GNN)-based path embedding framework (GNN-PE), which allows efficient exact subgraph matching without introducing false dismissals. Unlike traditional GNN-based graph embeddings that only produce approximate subgraph matching results, in this paper, we carefully devise GNN-based embeddings for paths, such that: if two paths (and 1-hop neighbors of vertices on them) have the subgraph relationship, their corresponding GNN-based embedding vectors will strictly follow the dominance relationship. With such a newly designed property of path dominance embeddings, we are able to propose effective pruning strategies based on path label/dominance embeddings and guarantee no false dismissals for subgraph matching. We build multidimensional indexes over path embedding vectors, and develop an efficient subgraph matching algorithm by traversing indexes over graph partitions in parallel and applying our pruning methods. We also propose a cost-model-based query plan that obtains query paths from the query graph with low query cost. Through extensive experiments, we confirm the efficiency and effectiveness of our proposed GNN-PE approach for exact subgraph matching on both real and synthetic graph data.