TY - GEN
T1 - α-Persistent Temporal Clique Enumeration with an Application
AU - Pal, Bithika
AU - Kolay, Sudeshna
AU - Banerjee, Suman
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Finding, Counting, and Enumerating different structural patterns in a large graph is a fundamental task in graph mining and forms the basis of many disciplines such as social network analysis, computational epidemiology, etc. Clique is one such structural pattern which is basically a subset of the vertices such that every pair of vertices in this subset is an edge in the graph. In practice, many graphs that we deal with are time-varying, i.e., the edge set of the graph is changing over time. To analyze the structural patterns of such graphs, the notion of temporal clique has been introduced. In this paper, we define the concept of α-Persistent Temporal Clique (α-PT Clique) in a binary node-attributed temporal network and propose an enumeration strategy for such cliques present in a given temporal network. The correctness of the proposed methodology has been illustrated and the complexity analysis has been done. Several experiments have been conducted with real-world temporal network datasets to illustrate the efficiency and effectiveness of the proposed solution approach. We have also demonstrated that α-PT Clique enumeration will be useful to choose Top-k people to be vaccinated to reduce the propagation of pandemic.
AB - Finding, Counting, and Enumerating different structural patterns in a large graph is a fundamental task in graph mining and forms the basis of many disciplines such as social network analysis, computational epidemiology, etc. Clique is one such structural pattern which is basically a subset of the vertices such that every pair of vertices in this subset is an edge in the graph. In practice, many graphs that we deal with are time-varying, i.e., the edge set of the graph is changing over time. To analyze the structural patterns of such graphs, the notion of temporal clique has been introduced. In this paper, we define the concept of α-Persistent Temporal Clique (α-PT Clique) in a binary node-attributed temporal network and propose an enumeration strategy for such cliques present in a given temporal network. The correctness of the proposed methodology has been illustrated and the complexity analysis has been done. Several experiments have been conducted with real-world temporal network datasets to illustrate the efficiency and effectiveness of the proposed solution approach. We have also demonstrated that α-PT Clique enumeration will be useful to choose Top-k people to be vaccinated to reduce the propagation of pandemic.
KW - Enumeration Algorithm
KW - Node Importance
KW - Temporal Network
KW - α-Persistent Temporal Clique
UR - https://www.scopus.com/pages/publications/85213401889
U2 - 10.1007/978-981-96-1242-0_27
DO - 10.1007/978-981-96-1242-0_27
M3 - Conference contribution
AN - SCOPUS:85213401889
SN - 9789819612413
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 359
EP - 371
BT - Databases Theory and Applications - 35th Australasian Database Conference, ADC 2024, Proceedings
A2 - Chen, Tong
A2 - Cao, Yang
A2 - Nguyen, Quoc Viet Hung
A2 - Nguyen, Thanh Tam
PB - Springer Science and Business Media Deutschland GmbH
T2 - 35th Australasian Database Conference, ADC 2024
Y2 - 16 December 2024 through 18 December 2024
ER -