Neighborhood Topology to Discover Influential Nodes in a Complex Network
This paper addresses the issue of distinguishing influential nodes in the complex network. The k-shell index features embeddedness of a node in the network based upon its number of links with other nodes. This index filters out the most influential nodes
- PDF / 275,819 Bytes
- 10 Pages / 439.37 x 666.142 pts Page_size
- 43 Downloads / 213 Views
Abstract This paper addresses the issue of distinguishing influential nodes in the complex network. The k-shell index features embeddedness of a node in the network based upon its number of links with other nodes. This index filters out the most influential nodes with higher values for this index, however, fails to discriminate their scores with good resolution, hence results in assigning same scores to the nodes belonging to same k-shell set. Extending this index with neighborhood coreness of a node and also featuring topological connections between its neighbors, our proposed method can express the nodes influence score precisely and can offer distributed and monotonic rank orders than other node ordering methods. Keywords Influential nodes Topological connections
⋅
k-shell index
⋅
Neighborhood coreness
⋅
1 Introduction The most authoritative nodes in a network can accomplish the speed and span of information diffusion when matched with other nodes [1–4]. Locating these influential nodes is of theoretical and practical significance in controlling the spread of information [5], ranking reputation of scientists [6], finding social leaders [7], developing efficient strategies to control epidemic spreading [8], promoting new products [9], and so on. Years of innovation have refined approaches to identify highly influential nodes to the consequences of spreading process on a given network. Many centrality indicators have been proposed to measure the estimated C. Saxena (✉) ⋅ M.N. Doja ⋅ T. Ahmad (✉) Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India e-mail: [email protected] T. Ahmad e-mail: [email protected] M.N. Doja e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 S.C. Satapathy et al. (eds.), Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Advances in Intelligent Systems and Computing 515, DOI 10.1007/978-981-10-3153-3_32
323
324
C. Saxena et al.
importance of nodes within the network, such as degree centrality (dc), closeness centrality (cc), betweenness centrality (bc) and eigenvalue centrality (ec), etc. The important issue is how to determine and distinguish the spreading capability of a node. Nodes having high centrality scores are notified to be more competent in the spreading process. Among these measures, dc is elementary and efficient, but based only on local structure information hence fails to identify influential nodes. Based on global link information bc is difficult to apply on large-sized networks. If there are disconnected components in networks, then cc has limitations to measure true centralities. If there are two or more nonidentical elements, the eigenvectors of the nodes adjacency matrix will mark only one of the elements, as a result ec interprets wrong results. Other centrality criteria are also on hand, such as neighborhood centrality [10], page rank [11], HITS [12], leader rank [7], semi-local centrality [13], and so on. Recently, Kitsak et al. [5] found that the most prestigious nod
Data Loading...
