A Survey on Distributed Graph Pattern Matching in Massive Graphs

Bouhenni, Sarra; Yahiaoui, Saïd; Nouali-Taboudjemat, Nadia; Kheddouci, Hamamache

doi:10.1145/3439724

Cited by 22 publications

(8 citation statements)

References 94 publications

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“…Some of these approaches are vertex-centric while others are edge-centric. Hybrid approaches have also been proposed [ 54 ]. Other studies have focused on improving the performance of these approaches [ 55 ].…”

Section: The Proposed Approachmentioning

confidence: 99%

Detection of fickle trolls in large-scale online social networks

Shafiei

Dadlani

2022

J Big Data

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Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users’ data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is $$O(n^3)$$ O ( n 3 ) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

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Section: The Proposed Approachmentioning

confidence: 99%

Detection of fickle trolls in large-scale online social networks

Shafiei

Dadlani

2022

J Big Data

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“…Myriad formulas and procedures exist for computing the net frequencies of graphlets [1], [7], [14], [17], [18], [20], [22], [25], [28], [31], [32]. They can be categorized into three types.…”

Section: Relations To Previous Workmentioning

confidence: 99%

“…Applied network analysis and graph data mining with motifs or graphlet distributions remain ad hoc and in flux, by and large, with undiminished interest and enthusiasm yet lack of coherent understanding and principled decision making at multiple data analysis stages. This situation is reflected in multiple surveys and reviews [1], [7], [18], [20], [31], [32]. Rarely graphlet frequencies are Figure 1: Triangle-frequency map (left) on Zachary's karate club friendship network [36] and the triangle-frequency sequence (right).…”

Section: Introductionmentioning

confidence: 99%

A systematic association of subgraph counts over a network

Floros,

Pitsianis,

Sun

2021

Preprint

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We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings.

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“…Various large-scale graphs for processing have been conducted, as large volumes of dynamic graph are continually generated [15][16][17][18][19][20]. The distributed graph processing system was developed to store a large amount of graphs in a distributed manner and analyze the distributed graphs in parallel [21][22][23][24]. PowerGraph is a distributed processing system that stores subgraphs in a distributed manner across multiple servers and processes the graph in parallel to overcome the limitations of a single server system [16].…”

Section: Introductionmentioning

confidence: 99%

Cost Model Based Incremental Processing in Dynamic Graphs

et al. 2022

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Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.

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A Survey on Distributed Graph Pattern Matching in Massive Graphs

Cited by 22 publications

References 94 publications

Detection of fickle trolls in large-scale online social networks

Detection of fickle trolls in large-scale online social networks

A systematic association of subgraph counts over a network

Cost Model Based Incremental Processing in Dynamic Graphs

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