In-memory dynamic graph processing faces three critical challenges: limited DRAM capacity, inefficient concurrent update/query handling, and vulnerability to crashes. Traditional segment-level systems struggle with write amplification on emerging Storage-Class Memory (SCM), while existing persistent-memory systems suffer from coarse-grained synchronization and high recovery overhead. This study presents the Storage-Class Memory Dynamic Graph (SMDG) processing framework, an architecture-level redesign centered on the block as the atomic unit across storage, concurrency, and recovery. The system addresses these challenges through three key innovations. First, a block-granular storage design organizes adjacency data at fixed-size block granularity on heterogeneous DRAM-SCM architecture, employing buffered batched writes to significantly reduce write amplification while preserving logarithmic update complexity. Second, block-level multi-version concurrency control maintains timestamped block versions under per-vertex read-write synchronization to provide task-ordered snapshot visibility for concurrent queries without copying entire vertices or pages. Third, a block-granular crash recovery protocol with decentralized per-vertex logs enables independent parallel reconstruction, ensuring application-level semantic consistency while achieving substantially faster recovery than sequential approaches. Experimental results validate that this unified block-granular design improves update efficiency, sustains mixed update-query workloads with controlled memory overhead, and accelerates crash recovery compared with prior dynamic graph systems.

This paper addresses the pressing need for effective k-tips decomposition in dynamic bipartite graphs, a crucial aspect of real-time applications that analyze and mine binary relationship patterns. Recognizing the dynamic nature of these graphs, our study is the first to provide a solution for k-tips decomposition in such evolving environments. We introduce a pioneering projection-based algorithm, coupled with advanced incremental maintenance strategies for edge modifications, tailored specifically for dynamic graphs. This novel approach not only fills a significant gap in the analysis of dynamic bipartite graphs but also substantially enhances the accuracy and timeliness of data-driven decisions in critical areas like public health. Our contributions set a new benchmark in the field, paving the way for more nuanced and responsive analyses in various domains reliant on dynamic data interpretation.

This paper addresses the pressing need for effective k-tips decomposition in dynamic bipartite graphs, a crucial aspect of real-time applications that analyze and mine binary relationship patterns. Recognizing the dynamic nature of these graphs, our study is the first to provide a solution for k-tips decomposition in such evolving environments. We introduce a pioneering projection-based algorithm, coupled with advanced incremental maintenance strategies for edge modifications, tailored specifically for dynamic graphs. This novel approach not only fills a significant gap in the analysis of dynamic bipartite graphs but also substantially enhances the accuracy and timeliness of data-driven decisions in critical areas like public health. Our contributions set a new benchmark in the field, paving the way for more nuanced and responsive analyses in various domains reliant on dynamic data interpretation.

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph; these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms.

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage and efficiency. In this article, we investigate distributed approaches to core maintenance on a pregel-like system, which is a famous graph computing system. We first design a core decomposition algorithm to obtain core numbers of vertices in a given graph. Based on it, a distributed batch-stream combined algorithm (DBCA) is devised to efficiently maintain the core numbers when vertex/edge updates happen. In particular, we introduce a new task assignment strategy to DBCA based on diversity of the edge-cores of updated edges. To ensure that DBCA can accurately process core maintenance, we develop a message interaction protocol to resolve the problem of crosstalk among different tasks. Comprehensive experiments have been conducted on real/synthetic graphs, more specifically, in two typical distributed environments built on Supercomputing Center and Alibaba Cloud. The experiment results demonstrate that our proposed algorithms are efficient and scalable.