Critical Node Identification based on Articulation Point Detection for Uncertain Network

The problem of efficiently identifying critical nodes that substantially degrade network performance if they do not function is crucial and essential in analyzing a large complex network such as social networks on the Web and road network in the real world, and it is still challenging. In this paper, we tackle this problem under a realistic situation where each link is probabilistically disconnected as assumed in studies in uncertain graphs. This reflects that in case of a social network an information path between two persons is not always open and may not pass on any information from one to the other and in case of a road network a road between two intersections is not always travelable and may be blocked by a traffic accident, a road repair, a nearby construction, etc. To solve this problem, we focus on the articulation point and utilize the bridge detection technique in graph theory to efficiently identify critical nodes when the node reachability is taken as the performance measure. In case of a social network disfunction of a node causes loss of the total number of people receiving information and in case of a road network it causes loss of the total number of people movable to other places. Using two real-world social networks and one road network, we empirically show that the proposed method has a good scalability with respect to the network size and the nodes our method identified possesses unique properties and they are difficult to be identified by using conventional centrality measures.

[1]  Robert E. Tarjan,et al.  A Note on Finding the Bridges of a Graph , 1974, Inf. Process. Lett..

[2]  Anastasios Gounaris,et al.  Mining Uncertain Graphs: An Overview , 2016, ALGOCLOUD.

[3]  Masahiro Kimura,et al.  Maximizing Network Performance Based on Group Centrality by Creating Most Effective k-Links , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[4]  Masahiro Kimura,et al.  Super mediator - A new centrality measure of node importance for information diffusion over social network , 2016, Inf. Sci..

[5]  George Kollios,et al.  k-nearest neighbors in uncertain graphs , 2010, Proc. VLDB Endow..

[6]  Yiming Yang,et al.  The Enron Corpus: A New Dataset for Email Classi(cid:12)cation Research , 2004 .

[7]  Laks V. S. Lakshmanan,et al.  Information and Influence Propagation in Social Networks , 2013, Synthesis Lectures on Data Management.

[8]  Lei Chen,et al.  On Uncertain Graphs , 2018, On Uncertain Graphs.

[9]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[10]  Nam P. Nguyen,et al.  On the Discovery of Critical Links and Nodes for Assessing Network Vulnerability , 2013, IEEE/ACM Transactions on Networking.

[11]  Masahiro Kimura,et al.  Critical Node Identification Based on Articulation Point Detection for Network with Uncertain Connectivity , 2018, 2018 Sixth International Symposium on Computing and Networking (CANDAR).

[12]  Ümit V. Çatalyürek,et al.  Graph Manipulations for Fast Centrality Computation , 2017, ACM Trans. Knowl. Discov. Data.

[13]  V. Latora,et al.  Centrality measures in spatial networks of urban streets. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[15]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[16]  Masahiro Kimura,et al.  Blocking links to minimize contamination spread in a social network , 2009, TKDD.

[17]  Licinio da Silva Portugal,et al.  Determining Critical Links in a Road Network: Vulnerability and Congestion Indicators☆ , 2014 .

[18]  Masahiro Kimura,et al.  Accelerating Computation of Distance Based Centrality Measures for Spatial Networks , 2016, DS.

[19]  Charu C. Aggarwal,et al.  Reliable clustering on uncertain graphs , 2012, 2012 IEEE 12th International Conference on Data Mining.

[20]  Masahiro Kimura,et al.  Detecting Critical Links in Complex Network to Maintain Information Flow/Reachability , 2016, PRICAI.

[21]  Orhan Dagdeviren,et al.  Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks , 2013, Sensors.

[22]  Masahiro Kimura,et al.  An Accurate and Efficient Method to Detect Critical Links to Maintain Information Flow in Network , 2017, ISMIS.