TY - GEN
T1 - Exploiting reshaping subgraphs from bilateral propagation graphs
AU - Hosseini, Saeid
AU - Yin, Hongzhi
AU - Cheung, Ngai Man
AU - Leng, Kan Pak
AU - Elovici, Yuval
AU - Zhou, Xiaofang
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Given a graph over which defects, viruses, or contagions spread, leveraging a set of highly correlated subgraphs is an appealing research area with many applications. However, the challenges abound. Firstly, an initial defect in one node can cause different defects in other nodes. Second, while the time is the most significant medium to understand diffusion processes, it is not clear when the members of a subgraph may change. Third, given a pair of nodes, a contagion can spread in both directions. Previous works only consider the sequential time-window and suppose that the contagion may spread from one node to the other during a predefined time span. But the propagation can differ in various temporal dimensions (e.g. hours and days). Therefore, we propose a framework that takes both sequential and multi-aspect attributes of the time into consideration. Moreover, we devise an empirical model to estimate how frequently the subgraphs may reshape. Experiment show that our framework can effectively leverage the reshaping subgraphs.
AB - Given a graph over which defects, viruses, or contagions spread, leveraging a set of highly correlated subgraphs is an appealing research area with many applications. However, the challenges abound. Firstly, an initial defect in one node can cause different defects in other nodes. Second, while the time is the most significant medium to understand diffusion processes, it is not clear when the members of a subgraph may change. Third, given a pair of nodes, a contagion can spread in both directions. Previous works only consider the sequential time-window and suppose that the contagion may spread from one node to the other during a predefined time span. But the propagation can differ in various temporal dimensions (e.g. hours and days). Therefore, we propose a framework that takes both sequential and multi-aspect attributes of the time into consideration. Moreover, we devise an empirical model to estimate how frequently the subgraphs may reshape. Experiment show that our framework can effectively leverage the reshaping subgraphs.
KW - Diffusion networks
KW - Propagation graphs
KW - Reshaping subgraphs
UR - https://www.scopus.com/pages/publications/85048049418
U2 - 10.1007/978-3-319-91452-7_23
DO - 10.1007/978-3-319-91452-7_23
M3 - Conference contribution
AN - SCOPUS:85048049418
SN - 9783319914510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 342
EP - 351
BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
A2 - Manolopoulos, Yannis
A2 - Li, Jianxin
A2 - Sadiq, Shazia
A2 - Pei, Jian
PB - Springer Verlag
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Y2 - 21 May 2018 through 24 May 2018
ER -