Attributed subgraph matching is a powerful tool for explorative mining of large attributed networks. In many applications (e.g., network science of teams, intelligence analysis, finance informatics), the user might not know what exactly s/he is looking for, and thus require the user to constantly revise the initial query graph based on what s/he finds from the current matching results. A major bottleneck in such an interactive matching scenario is the efficiency, as simply rerunning the matching algorithm on the revised query graph is computationally prohibitive. In this paper, we propose a family of effective and efficient algorithms (FIRST) to support interactive attributed subgraph matching. There are two key ideas behind the proposed methods. The first is to recast the attributed subgraph matching problem as a cross-network node similarity problem, whose major computation lies in solving a Sylvester equation for the query graph and the underlying data graph. The second key idea is to explore the smoothness between the initial and revised queries, which allows us to solve the new/updated Sylvester equation incrementally, without re-solving it from scratch. Experimental results show that our method can achieve (1) up to 16x speed-up when applying on networks with 6M$+$ nodes; (2) preserving more than 90% accuracy compared with existing methods; and (3) scales linearly with respect to the size of the data graph.
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Identifying Bottleneck Nodes using Packet Delay Statistics
An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse.
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- Award ID(s):
- 1642973
- NSF-PAR ID:
- 10202117
- Date Published:
- Journal Name:
- Proceedings of the IEEE Military Communications Conference (MILCOM)
- Page Range / eLocation ID:
- 447 to 452
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MILCOM47813.2019.9021045
