Distributed hop-constrained s-t simple path enumeration at billion scale

Hao, Kongzhang; Yuan, Long; Zhang, Wenjie

doi:10.14778/3489496.3489499

Cited by 24 publications

(4 citation statements)

References 53 publications

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“…While graph algorithms provide a more direct means of capturing relationships within graph networks, they often face challenges when dealing with large-scale graphs. In recent years, numerous parallel graph algorithms have emerged, and a distributed search approach ( Hao, Yuan & Zhang, 2021 ) based on graph partitioning has introduced a novel solution for parallel searching. Previously, it was considered difficult to parallelize dictionary or ordered DFS ( Reif, 1985 ).…”

Section: Related Workmentioning

confidence: 99%

Parallel path detection for fraudulent accounts in banks based on graph analysis

Chen,

Zhang,

Zeng

et al. 2023

PeerJ Computer Science

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This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

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Section: Related Workmentioning

confidence: 99%

Parallel path detection for fraudulent accounts in banks based on graph analysis

Chen,

Zhang,

Zeng

et al. 2023

PeerJ Computer Science

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“…Thus, the top-r k-influential community search problem is NP-hard, when f (•) = avg. 5 to denote the objective function of the top-r k-influential community search problem, where δ(H) stands for the minimum degree of the subgraph H and f (H) is the aggregation function. Then we could obtain the following theorems: Theorem 2.…”

Section: Problem Hardnessmentioning

confidence: 99%

Finding Top-r Influential Communities under Aggregation Functions

Peng¹,

Bian²,

Li³

et al. 2022

Preprint

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Community search is a problem that seeks cohesive and connected subgraphs in a graph that satisfy certain topology constraints, e.g., degree constraints. The majority of existing works focus exclusively on the topology and ignore the nodes' influence in the communities. To tackle this deficiency, influential community search is further proposed to include the node's influence. Each node has a weight, namely influence value, in the influential community search problem to represent its network influence. The influence value of a community is produced by an aggregated function, e.g., max, min, avg, and sum, over the influence values of the nodes in the same community. The objective of the influential community search problem is to locate the top-r communities with the highest influence values while satisfying the topology constraints. Existing studies on influential community search have several limitations: (i) they focus exclusively on simple aggregation functions such as min, which may fall short of certain requirements in many real-world scenarios, and (ii) they impose no limitation on the size of the community, whereas most real-world scenarios do. This motivates us to conduct a new study to fill this gap.We consider the problem of identifying the top-r influential communities with/without size constraints while using more complicated aggregation functions such as sum or avg. We give a theoretical analysis demonstrating the hardness of the problems and propose efficient and effective heuristic solutions for our topr influential community search problems. Extensive experiments on real large graphs demonstrate that our proposed solution is significantly more efficient than baseline solutions.

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“…To solve the problem, the data-based approaches are initially investigated on faults diagnosis for hydro-power plants, i.e., mining information in data and training models [4]. The database-aided approaches can achieve high diagnosis accuracy without requiring efficient expert knowledge [5]. However, data-based approaches also have two problems.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme

Guo,

Wang,

Pan

et al. 2023

J. vibroeng.

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In hydro-power systems, the fault of equipment is an important potential threat for the safe production of electricity. Therefore, the automation and intelligence of fault diagnosis becomes the popular issue in the research on hydro-power system. In this paper, a knowledge graph-based method is put forth to diagnose faults occurred in hydro-power systems, since the knowledge graph can store structured and unstructured data for better fault diagnosis and intelligently search the reasons of the faults. First, we model the knowledge graph for hydro-power plants, where the rational path for the fault reason is formulated. Then, the bi-directional long short-term memory (Bi-LSTM) with conditional random field (CRF) is used to extract the entities and relations to the given documents, which record the phenomenon and reasons for the occurred faults. Moreover, the attention scheme is employed in the Bi-LSTM to weigh the closer relationships to improve the diagnosis accuracy. An automatic diagnosis algorithm is developed to improve the diagnosing efficiency by constructing rational paths, with which directive and in-directive factors for occurring faults can be traced. Simulation results reveal that the intelligent search method with a knowledge graph can effectively find the reason, locate the position, and provide useful suggestions for the occurred faults.

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Distributed hop-constrained s-t simple path enumeration at billion scale

Cited by 24 publications

References 53 publications

Parallel path detection for fraudulent accounts in banks based on graph analysis

Parallel path detection for fraudulent accounts in banks based on graph analysis

Finding Top-r Influential Communities under Aggregation Functions

Fault diagnosis method for hydro-power plants with Bi-LSTM knowledge graph aided by attention scheme

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