A survey of community search over big graphs

Fang, Yixiang; Huang, Xin; Qin, Lu; Zhang, Ying; Zhang, Wenjie; Cheng, Reynold; Lin, Xuemin

doi:10.1007/s00778-019-00556-x

Cited by 239 publications

(108 citation statements)

References 209 publications

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“…Self-attention (SA) mechanism was originally proposed for machine translation [Vaswani et al, 2017], then it has been successfully applied in computer vision, such as video analysis [Wang et al, 2018b], image segmentation [Fu et al, 2019], and image generation . SA also has a close relationship to community search [Fang et al, 2020b;Fang et al, 2020a]. The implementation of SA mechanism for CNNs is as follows.…”

Section: Self-attention Mechanismmentioning

confidence: 99%

Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation

Chen

Gong

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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In this paper, we apply self-attention (SA) mechanism to boost the performance of deep metric learning. However, due to the pairwise similarity measurement, the cost of storing and manipulating the complete attention maps makes it infeasible for large inputs. To solve this problem, we propose a compressed self-attention with low-rank approximation (CSALR) module, which significantly reduces the computation and memory costs without sacrificing the accuracy. In CSALR, the original attention map is decomposed into a landmark attention map and a combination coefficient map with a small number of landmark feature vectors sampled from the input feature map by average pooling. Thanks to the efficiency of CSALR, we can apply CSALR to high-resolution shallow convolutional layers and implement a multi-head form of CSALR, which further boosts the performance. We evaluate the proposed CSALR on person reidentification which is a typical metric learning task. Extensive experiments shows the effectiveness and efficiency of CSALR in deep metric learning and its superiority over the baselines.

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Section: Self-attention Mechanismmentioning

confidence: 99%

Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation

Chen

Gong

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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“…Other approaches were performed by influential nodes and similar profiles between nodes. 7,8 In this section of this article, we have classified community detection methods based on the problem-solving approach.…”

Section: Related Workmentioning

confidence: 99%

“…They are classified according to different criteria as k-core, location, k-truss, k-clique, K-ECC, etc. 8 We can consider influence maximization for goal function in optimization-based methods. 31 Influence maximization is an optimization problem to discover a group of nodes in a social network that has a maximum influence considering a propagation model.…”

Section: Communities Detection By Futuristic Approachmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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“…Other Dense Subgraphs. Recently, many other dense subgraph models [24], such as k-core [7,47,51,23,22,20,25,26,67,12], k-truss [15,37,69,39,38], k-(r, s) nucleus [60,58,61,59] (a generalization of k-core and k-truss), k-clique [16,34], k-edge connected components [35,36]. and k-plexes [63], have also been explored.…”

Section: Related Workmentioning

confidence: 99%

Efficient algorithms for densest subgraph discovery

Fang

Cheng

et al. 2019

Proc. VLDB Endow.

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Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that the densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based-and general pattern-based density. We have performed extensive experimental evaluation on both real and synthetic datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.

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A survey of community search over big graphs

Cited by 239 publications

References 209 publications

Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation

Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Efficient algorithms for densest subgraph discovery

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