Search For Community Research Articles

Conditional Community Search Based on Weight Information

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some complex search conditions based on Boolean expressions. For example, certain desired nodes should be contained in the communities, while certain undesired nodes cannot exist in the communities, which is called conditional community search. However, existing solutions for conditional community search often introduce some undesired nodes into the identified communities and exhibit relatively low search efficiency. To overcome these drawbacks, therefore, this paper investigates the problem of conditional community search based on weight information. First, we refine the original problem definition of conditional community search and outline the need for an improved algorithm for calculating the weights of the nodes. Then, we explore two novel algorithms for searching conditional communities based on calculated weight information. Finally, we conduct extensive experiments on several real-world datasets to verify the accuracy and efficiency of our proposed searching algorithms.

A D-Truss-Equivalence Based Index for Community Search Over Large Directed Graphs

A D-Truss-Equivalence Based Index for Community Search Over Large Directed Graphs

Truss community search in uncertain graphs

Truss community search in uncertain graphs

African Pastors’ Virtual Ministry from South Korea

The massive shift of ministry from physical to virtual spaces as occasioned by the Covid-19 pandemic led to an explosion in virtual ministry. The proliferation of social media within diverse areas of human life continues to present important ramifications for present and future church ministry. This article presents the findings of a qualitative research study into the virtual ministry of thirteen African pastors in Korea to their people back home and in so doing highlights the importance of Christian mission from an African diasporic view. The purpose of this study was to understand the experiences of African pastors in Korea who conducted virtual ministry to their congregation back home in the time of Covid-19. The grounded theory of this study was that African virtual pastoring from Korea during Covid-19 was driven by a search for community, used the prevalent and yet unreliable or problematic social media platforms, as a means to carry out interactive ministries and often extended beyond their home audiences. It is also revealed that the African pastors forged a mediated space through their virtual ministry even as they negotiated their distance and belonging in South Korean society.

TÌM KIẾM CỘNG ĐỒNG MẠNG DỰA TRÊN CẢI TIẾN TOẠ ĐỘ CỦA ĐỈNH

In recent years, with the strong development of information technology, detecting communities in large real networks is a very important issue which is of interest to many scientists. Community detection in large real networks with millions of nodes is often difficult. To solve this problem, many online community search algorithms have been proposed with many different approaches. One of the approaches is to coordinate the vertices of the graph and build a reasonable distance between those vertices. It has been observed that vertices in the same community have approximately the same probability of reaching other vertices through a random walk. Based on this principle, the authors propose a way to coordinate vertices and build distances between vertices in the graph that reduces computational complexity compared to existing techniques. This approach involves representing peaks as vectors and using the Kmeans++ algorithm for community detection, whose effectiveness is evaluated through experimental results presented.

Efficient Parallel D-Core Decomposition at Scale

Directed graphs are prevalent in social networks, web networks, and communication networks. A well-known concept of the directed graph is the D-core, or ( k, l )-core, which is the maximal subgraph in which each vertex has an in-degree not less than k and an out-degree not less than l. Computing the non-empty D-cores for all possible values of k and l , a.k.a. D-core decomposition, has found versatile applications spanning social network analysis, community search, and graph visualization. However, existing algorithms of D-core decomposition suffer from efficiency and scalability issues on large graphs, because serial peeling-based algorithms are limited by single-core utilization, while skyline coreness-based methods exhibit notably high time complexity. To tackle these issues, in this paper, we propose efficient parallel algorithms for D-core decomposition by leveraging the computational prowess of multicore CPUs. Specifically, we first propose a novel algorithm that computes the D-cores for each possible k value, by exploiting an implicit level-by-level vertex removal strategy, which not only diminishes dependencies between vertices but also maintains a time complexity akin to that of sequential algorithms. We further develop an advanced algorithm by introducing a novel concept of D-shell, which allows us to curtail redundant computations by reducing the necessary k values when computing corresponding D-cores, and deriving D-cores with larger k values from the D-cores currently computed based on D-shell. Extensive experiments on ten real-world large graphs show that our algorithms are highly efficient and scalable, and the advanced algorithm is up to two orders of magnitude faster than the state-of-the-art parallel decomposition algorithm with 32 threads.

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 query nodes and query attributes. Previously, algorithmic approaches deal with ACS in a two-stage paradigm, which suffer from structural inflexibility and attribute irrelevance. To overcome this problem, recently, learning-based approaches have been proposed to learn both structures and attributes simultaneously as a one-stage paradigm. However, these approaches train a transductive model which assumes the graph to infer unseen queries is as same as the graph used for training. That limits the generalization and adaptation of these approaches to different heterogeneous graphs. In this paper, we propose a new framework, Inductive Attributed Community Search, IACS , by inductive learning, which can be used to infer new queries for different communities/graphs. Specifically, IACS employs an encoder-decoder neural architecture to handle an ACS task at a time, where a task consists of a graph with only a few queries and corresponding ground-truth. We design a three-phase workflow, "training-adaptation-inference", which learns a shared model to absorb and induce prior effective common knowledge about ACS across different tasks. And the shared model can swiftly adapt to a new task with small number of ground-truth. We conduct substantial experiments in 7 real-world datasets to verify the effectiveness of IACS for CS/ACS. Our approach IACS achieves 28.97% and 25.60% improvements in F1-score on average in CS and ACS, respectively.

Maximal size constraint community search over bipartite graphs

Community search over bipartite graphs is a crucial issue with significant applications in various domains, including group recommendations, fraud detection and representation learning. Current research primarily focuses on the structural cohesiveness constraint between two sets of vertices, often neglecting the community’s size constraint. Unfortunately, this oversight may lead to communities of sizes ranging from excessively small to substantially large, thereby affecting their applicability. Therefore, in this paper, we focus on the maximal size constraint community search over bipartite graphs, which not only takes into account the (α,β) degree constraints for each set of vertices but also ensures that the number of vertices in each set is limited to ξ and ζ, respectively. To solve this problem, we first introduce two competitive algorithms, namely Ordering-based Expansion Algorithm (OEA) and Followers-based Peeling Algorithm (FPA), wherein they greedily expand or peel vertices, respectively, to identify the maximal one in a breadth-first manner, utilizing a novel vertex ordering and followers concept. To further accelerate the computation, we propose an advanced Expand-and-Filter Algorithm (EFA++) that first acquires a slightly larger community and subsequently executes a filtering phase employing a dynamic programming technique. Furthermore, we define the “Affected Area” for edge insertion or deletion that caters for dynamic scenarios. Finally, the theoretical analysis and extensive experimental evaluation on several real-life datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

On Density-based Local Community Search

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 measurement of S such as the conductance and the density. An ideal algorithm for optimizing f(S) is strongly local, that is, the complexity is dependent on R as opposed to G. This paper formulates a general form of objective functions for LCS using configurations and then focuses on a set C of density-based configurations, each corresponding to a density-based LCS objective function. The paper has two main results. i) A constructive classification of C: a configuration in C has a strongly local algorithm for optimizing its corresponding objective function if and only if it is in C L ⊆ C. ii) A linear programming-based general solution for density-based LCS that is strongly local and practically efficient. This solution is different from the existing strongly local LCS algorithms, which are all based on flow networks.

Efficient Unsupervised Community Search with Pre-Trained Graph Transformer

Community search has aroused widespread interest in the past decades. Among existing solutions, the learning-based models exhibit outstanding performance in terms of accuracy by leveraging labels to 1) train the model for community score learning, and 2) select the optimal threshold for community identification. However, labeled data are not always available in real-world scenarios. To address this notable limitation of learning-based models, we propose a pre-trained graph Trans former based community search framework that uses Zero label (i.e., unsupervised), termed TransZero. TransZero has two key phases, i.e., the offline pre-training phase and the online search phase. Specifically, in the offline pre-training phase, we design an efficient and effective community search graph transformer ( CSGphormer ) to learn node representation. To pre-train CSGphormer without the usage of labels, we introduce two self-supervised losses, i.e., personalization loss and link loss, motivated by the inherent uniqueness of node and graph topology, respectively. In the online search phase, with the representation learned by the pre-trained CSGphormer , we compute the community score without using labels by measuring the similarity of representations between the query nodes and the nodes in the graph. To free the framework from the usage of a label-based threshold, we define a new function named expected score gain to guide the community identification process. Furthermore, we propose two efficient and effective algorithms for the community identification process that run without the usage of labels. Extensive experiments over 10 public datasets illustrate the superior performance of TransZero regarding both accuracy and efficiency.

CCSS: Towards conductance-based community search with size constraints

Size-constrained community search, retrieving a size-bounded high-quality subgraph containing user-specified query vertices, has been extensively studied in graph analysis. However, existing methods mainly focus on the cohesiveness within the community while ignoring its separation from the outside, leading to sub-optimal results. Inspired by this, we adopt the well-known conductance to measure the quality of a community and introduce a novel problem of Conductance-based Community Search with Size Constraints (CCSS). CCSS aims to identify a subgraph with the smallest conductance among all connected subgraphs that contain the query vertex q and have at least l and at most h vertices. We prove that CCSS is NP-hard. To efficiently address CCSS, we first propose a computational framework ISC with two-level loops. In the inner loop, ISC starts from q and iteratively adds vertices based on the predefined score function, obtaining a coarse-grained candidate community. In the outer loop, we design the perturbation strategy to repeatedly execute the inner loop until the quality of the candidate community converges. Then, we propose two heuristic algorithms, ISCCI and ISCCP, to concretely implement the score function and perturbation strategy of ISC, which significantly reduce the computational costs. Finally, empirical results demonstrate the superiorities of our solutions. Our source codes and datasets are available at https://github.com/longlonglin/CCSS.

"This might be cliché, but it was a sense of family": Gang involvement among Indigenous young adults and their search for attachment, community, and hope.

Indigenous communities in Canada continue to feel the ongoing impacts of colonialism, including socio-economic disadvantage, high rates of violent victimization, systemic racism and discrimination, overrepresentation in the criminal justice system, and intergenerational trauma. Based on in-depth interviews with 10 gang-involved Indigenous young adults, using attachment theory as a guiding framework, we explore how colonialism continues to negatively impact the attachment these young people have to their families, communities, and social institutions, and leads to their gang involvement which perpetuates violence and trauma. Yet, they exhibit hope for a better future. Drawing on participant experiences we suggest key points at which provision of supports and resources can assist with increasing attachments and facilitating gang desistance. We share these insights while acknowledging the continued structural, embedded violence many Indigenous youth experience today that necessitates a commitment to decolonization at all levels of Canadian society.

Truss-Based Community Search over Streaming Directed Graphs

Truss-Based Community Search over Streaming Directed Graphs

Recommended by Librarians

To study library guides, as published on Springshare’s LibGuides platform, new approaches are needed to expand the scope of the research, ensure comprehensiveness of data collection, and reduce bias for content analysis. Computational methods can be utilized to conduct a nuanced and thorough evaluation that critically assesses the resources promoted in library guides. Web-based library guides are curated by librarians to provide easy access to high-quality information and resources in a variety of formats to support the research needs of their users. Recent scholarship considers library guides as valuable resources and as de facto publications, highlighting the need for critical study. In this article, the authors present a novel model for comprehensively gathering data about a specific genre of books from individual LibGuide pages and applying computational methods to explore the resultant data. Beginning with a pre-selected list of 159 books, we programmatically queried the titles using the LibGuides Community search engine. After cleaning and filtering the resultant data, we compiled a list of 20,484 book references (of which 6,212 are unique) on 1,529 LibGuide pages. By testing against inclusion and exclusion criteria to ensure relevancy, we identified a total of 281 titles relevant to our topic. To gain insights for future study, citation analysis metrics are presented to reveal patterns of frequency, co-occurrence, and bibliographic coupling of books promoted in LibGuides. This proof-of-concept could be adopted for a variety of applications, including assessment of collections, public services, critical librarianship, and other complex questions to enable a richer and more thorough understanding of the information landscape of LibGuides.

A biased edge enhancement method for truss-based community search

A biased edge enhancement method for truss-based community search

Leveraging Semantic Information for Enhanced Community Search in Heterogeneous Graphs

Community search (CS) is a vital research area in network science that focuses on discovering personalized communities for query vertices from graphs. However, existing CS methods mainly concentrate on homogeneous or simple attributed graphs, often disregarding complex semantic information and rich contents carried by entities in heterogeneous graphs (HGs). In this paper, we propose a novel problem, namely the “Semantic Network Oriented Community Search with Meta-Structures in Heterogeneous Graphs (SNCS),” which aims to find dense communities that contain the query vertex, with vertices of the same type sharing similar topics. In response to this new problem, we present a novel approach, also named SNCS, representing the first solution employing meta-structures and topic constraints to tackle community search, leveraging both topological and latent features. To overcome the high-time complexity challenge posed by searching through meta-structures, we introduce a unique graph reconstruction technique. Our proposed method’s superiority is validated through extensive evaluations on real-world datasets. The results demonstrate a significant improvement in the quality of the obtained communities, with increases of 3.5–4.4% in clustering coefficient and 5–11% in density while requiring only 4–46% of the running time when compared with the state-of-the-art methods.

An Algorithm for Finding Optimal k-Core in Attribute Networks

As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.

QTCS: Efficient Query-Centered Temporal Community Search

Temporal community search is an important task in graph analysis, which has been widely used in many practical applications. However, existing methods suffer from two major defects: (i) they only require that the target result contains the query vertex q , leading to the temporal proximity between q and other vertices being ignored. Thus, they may find many temporal irrelevant vertices (these vertices are called query-drifted vertices) concerning q for satisfying their objective functions; (ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. In this paper, we propose a new problem named query-centered temporal community search to overcome these limitations. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β -temporal proximity core, which can seamlessly combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β -temporal proximity core with the largest β. We theoretically prove that our problem can circumvent these query-drifted vertices. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions.

Retracted: Personalized Influential Community Search in Large Networks: A K-ECC-Based Model

Retracted: Personalized Influential Community Search in Large Networks: A K-ECC-Based Model

Efficient and effective [formula omitted]-core-based community search over attributed heterogeneous information networks

Given a heterogeneous information network (HIN) G and a query node q, community search (CS) over an HIN identifies a cohesive subgraph from G that contains q. Although HINs with attributes on nodes (called AHINs) are prevalent today, the CS over AHINs (CS-AHIN) is ignored in the literature. Though we can convert an AHIN to an attributed homogeneous graph given a meta-path, then apply the CS approaches for attributed homogeneous graphs to solve CS-AHIN, it is problematic for two reasons. (1) Complete graph conversion is time-consuming and unnecessary, because the search only involves the query node's neighborhood, not the entire graph. (2) Existing attribute cohesiveness metrics are not strict enough to reflect substantial similarities among the community's pairwise nodes. To resolve this, we define the CS-AHIN problem atop a strict attribute cohesiveness metric that supports textual and numerical attributes simultaneously. We show the problem is NP-hard. To address it, we propose an exact baseline to return the global optimal result. Then, we propose three heuristic algorithms using a general greedy search framework to speed up the efficiency. Moreover, we present a cohesiveness-aware proximity graph-based index to boost the performance. Comprehensive experimental studies on various real-world datasets demonstrate our method's superiority.