Computer Science ›› 2025, Vol. 52 ›› Issue (12): 115-124.doi: 10.11896/jsjkx.241100090

• Database & Big Data & Data Science • Previous Articles     Next Articles

PIEnum:Efficient Algorithm of Path Enumeration on Large Uncertain Graphs

XIE Wenlin, DU Ming, ZHOU Junfeng   

  1. School of Computer Science and Technology, Donghua University, Shanghai 201620, China
  • Received:2024-11-24 Revised:2025-02-15 Online:2025-12-15 Published:2025-12-09
  • About author:XIE Wenlin,born in 1997,postgra-duate.His main research interest is query processing on large scale graph data and optimization.
    DU Ming,born in 1975,Ph.D,professor.His main research interests include natural language processing and information analysis.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(62372101,61873337,62272097).

Abstract: A basic approach of investigating the relationship between two vertices on the uncertain graph is to enumerate all paths between them.To solve the problem of inadequate pruning and redundant computation in the state-of-the-art algorithms,this paper proposes an pruning and index based algorithm,PIEnum,whose target is to enumerate simple paths from a source vertex s to a target vertex t where the length of each path is no more than a given hop constraint k and the probability of each path is no less than a given probability threshold γ.For an input query,it firstly excludes the unpromising vertices to reduce the search space.Then it builds an online light-weight index to avoid repeated pruning examinations during the enumeration.Finally,it develops an efficient approach to prune invalid search branches during the enumeration.To further improve the performance on dense graphs,it implements PIEnum+ based on the Join paradigm.The comprehensive experimental results on 10 real-life graphs show that PIEnum improves the overall performance by at least 10 times compared to the state-of-the-art algorithms.

Key words: Graph, Uncertain graph, Path enumeration, Pruning, Index

CLC Number: 

  • TP301
[1]LIU H,JIN C,YANG B,et al.Finding top-k shortest paths with diversity[C]//ICDE.IEEE Computer Society,2018:1761-1762.
[2]OUYANG D,YUAN L,QIN L,et al.Efficient shortest path index maintenance on dynamic road networks with theoretical guarantees[J].VLDB Endow,2020,13(5):602-615.
[3]PENG Y,YU J X,WANG S.PSPC:efficient parallel shortest path counting on large-scale graphs[C]//ICDE.IEEE,2023:896-908.
[4]LAI L,QING Z,YANG Z,et al.Distributed subgraph matching on timely dataflow[J].VLDB Endow,2019.12(10):1099-1112.
[5]WANG K,LIN X,QIN L,et al.Vertex priority based butterfly counting for large-scale bipartite networks[J].VLDB Endow,2019,12(10):1139-1152.
[6]KAPUSTA J,MUNK M,SVEC P.Selection of suitable page-rank calculation for analysis of differences between expected and observed probability of accesses to web page[C]//MIWAI.Springer,2018:139-150.
[7]LIBEN D,KLEINBERG J M.The link-prediction problem forsocial networks[J].JASIST,2007,58(7):1019-1031.
[8]YAN J,WANG W,WU Z,et al.An uncertain graph method based on shuffle model to preserve link privacy of mobile social networks[J].JISE,2024,40(1):125-150.
[9]SEVON P,ERONEN L,HINTSANEN P,et al.Link discovery in graphs derived from biological databases[C]//DILS.Sprin-ger,2006:35-49.
[10]KIMURA M,SAITO K.Tractable models for information diffusion in social networks[C]//PKDD.Berlin:Springer,2006:259-271.
[11]SAHA A,BROKKELKAMP R,VELAJ Y,et al.Shortest paths and centrality in uncertain networks[J].VLDB Endow,2021,14(7):1188-1201.
[12]PENG Y,ZHANG Y,ZHANG W,et al.Efficient probabilistic k-core computation on uncertain graphs[C]//ICDE.IEEE Computer Society,2018:1192-1203.
[13]ZHOU A,WANG Y,CHEN L.Butterfly counting and bitruss decomposition on uncertain bipartite graphs[J].VLDB J,2023,32(5):1013-1036.
[14]KE X,KHAN A,QUAN L H.An in-depth comparison of s-t reliability algorithms over uncertain graphs[J].VLDB Endow,2019,12(8):864-876.
[15]DAI Q,LI R,LIAO M,CHEN H,et al.Fast maximal cliqueenumeration on uncertain graphs:A pivot-based approach[C]//SIGMOD.ACM,2022:2034-2047.
[16]YASUDA N,SUGAYA T,MINATO S.Fast compilation of s-t paths on a graph for counting and enumeration[C]//AMBN.PMLR,2017:129-140.
[17]BIRMELE E,FERREIRA R A,GROSSI R,et al.Optimal lis-ting of cycles and st-paths in undirected graphs[C]//SODA.SIAM,2013:1884-1896.
[18]NISHINO M,YASUDA N,MINATO S,et al.Compiling graph substructures into sentential decision diagrams[C]//AAAI.Springer,2017:1213-1221.
[19]GROSSI R,MARINO A,VERSARI L.Efficient algorithms for listing k disjoint st-paths in graphs[C]//LATIN.Springer,2018:544-557.
[20]RIZZI R,SACOMOTO G,SAGOT M.Efficiently listing boun-ded length st-paths[C]//IWOCA.Springer,2014:318-329.
[21]PENG Y,ZHANG Y,LIN X,et al.Hop-constrained s-t simple path enumeration:Towards bridging theory and practice[J].VLDB Endow,2019,13(4):463-476.
[22]QIU X,CEN W,QIAN Z,et al.Realtime constrained cycle detection in large dynamic graphs[J].VLDB Endow,2018,11(12):1876-1888.
[23]LAI Z,PENG Y,YANG S,et al.PEFP:efficient k hop constrained s-t simple path enumeration on FPGA[C]//ICDE.IEEE,2021:1320-1331.
[24]HAO K,YUAN L,ZHANG W.Distributed hop-constrained s-t simple path enumeration at billion scale[J].VLDB Endow,2021,15(2):169-182.
[25]SUN S,CHEN Y,HE B,et al.Pathenum:Towards real-time hop constrained s-t path enumeration[C]//SIGMOD.ACM,2021:1758-1770.
[26]ZHANG J,YANG S,OUYANG D,et al.Hop constrained s-t simple path enumeration on large dynamic graphs[C]//ICDE.IEEE,2023:762-775.
[27]LI X,HAO K,YANG Z,et al.Hop-constrained s-t simple path enumeration in large uncertain graphs[C]//ADC.Springer,2022:115-127.
[1] CAI Qihang, XU Bin, DONG Xiaodi. Knowledge Graph Completion Model Using Semantically Enhanced Prompts and Structural Information [J]. Computer Science, 2025, 52(9): 282-293.
[2] CHENG Zhangtao, HUANG Haoran, XUE He, LIU Leyuan, ZHONG Ting, ZHOU Fan. Event Causality Identification Model Based on Prompt Learning and Hypergraph [J]. Computer Science, 2025, 52(9): 303-312.
[3] ZHU Shihao, PENG Kexing, MA Tinghuai. Graph Attention-based Grouped Multi-agent Reinforcement Learning Method [J]. Computer Science, 2025, 52(9): 330-336.
[4] ZHOU Tao, DU Yongping, XIE Runfeng, HAN Honggui. Vulnerability Detection Method Based on Deep Fusion of Multi-dimensional Features from Heterogeneous Contract Graphs [J]. Computer Science, 2025, 52(9): 368-375.
[5] TANG Jiayi, HUANG Xiaofang, WANG Licheng, ODOOM J. Identity-based Linkable Ring Signcryption on NTRU Lattice [J]. Computer Science, 2025, 52(9): 396-404.
[6] LI Yaru, WANG Qianqian, CHE Chao, ZHU Deheng. Graph-based Compound-Protein Interaction Prediction with Drug Substructures and Protein 3D Information [J]. Computer Science, 2025, 52(9): 71-79.
[7] WU Hanyu, LIU Tianci, JIAO Tuocheng, CHE Chao. DHMP:Dynamic Hypergraph-enhanced Medication-aware Model for Temporal Health EventPrediction [J]. Computer Science, 2025, 52(9): 88-95.
[8] HU Hailong, XU Xiangwei, LI Yaqian. Drug Combination Recommendation Model Based on Dynamic Disease Modeling [J]. Computer Science, 2025, 52(9): 96-105.
[9] SU Shiyu, YU Jiong, LI Shu, JIU Shicheng. Cross-domain Graph Anomaly Detection Via Dual Classification and Reconstruction [J]. Computer Science, 2025, 52(8): 374-384.
[10] TANG Boyuan, LI Qi. Review on Application of Spatial-Temporal Graph Neural Network in PM2.5 ConcentrationForecasting [J]. Computer Science, 2025, 52(8): 71-85.
[11] WANG Pei, YANG Xihong, GUAN Renxiang, ZHU En. Deep Graph Contrastive Clustering Algorithm Based on Dynamic Threshold Pseudo-label Selection [J]. Computer Science, 2025, 52(8): 100-108.
[12] GUO Husheng, ZHANG Xufei, SUN Yujie, WANG Wenjian. Continuously Evolution Streaming Graph Neural Network [J]. Computer Science, 2025, 52(8): 118-126.
[13] MIAO Zhuqing, HAN Jingyu, LI Caiyun, WANG Yanzhi, MAO Yi, ZHANG Yiting. Douglas-Peuker Algorithm Based Learned Index Structure for Road Network Trajectroy Data [J]. Computer Science, 2025, 52(8): 136-145.
[14] CHEN Genshen, LIU Gang, DONG Yang, FAN Wenyao, YI Qiang, JIANG Zixin. Efficient Indexing Method for Massive 3D Geological Block Models Based on Inverted-B+ Tree [J]. Computer Science, 2025, 52(8): 146-153.
[15] YANG Feixia, LI Zheng, MA Fei. Research on Hyperspectral Image Super-resolution Methods Based on Tensor Ring SubspaceSmoothing and Graph Regularization [J]. Computer Science, 2025, 52(8): 240-250.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!