Information source detection in networks

Possibility and impossibility results

Kai Zhu, Lei Ying

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper studies information source detection in networks under the independent cascade (IC) model. Assume the spread of information starts from a single source in a network and a complete snapshot of the network is obtained at some time. The goal is to identify the source based on the observation. We derive the maximum a posterior (MAP) estimator of the source for tree networks and propose a Short-Fat Tree (SFT) algorithm for general networks based on the MAP estimator. The algorithm selects the Jordan infection center [1] and breaks ties according the degree of boundary infected nodes. Loosely speaking, the algorithm selects the node such that the breadth-first search (BFS) tree from it has the minimum depth but the maximum number of leaf nodes. On the Erdos-Renyi (ER) random graph, we establish the following possibility and impossibility results: (i) when the infection duration 0.5, SFT identifies the source with probability 1 (w.p.1) asymptotically (as network size increases to infinity), where n is the network size and μ is the average node degree; (ii) when the infection duration > [log n/ log μ ] + 2, the probability of identifying the source approaches zero asymptotically under any algorithm; and (iii) when infection duration < 0, asymptotically, at least 1-δ fraction of the nodes on the BFS tree starting from the source are leaf-nodes, where δ = 3√log n/μ, i.e., the BFS tree starting from the actual source is a fat tree. 1Numerical experiments on tree networks, the ER random graphs and real world networks with different evaluation metrics show that the SFT algorithm outperforms existing algorithms.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2016-July
ISBN (Electronic)9781467399531
DOIs
StatePublished - Jul 27 2016
Event35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016 - San Francisco, United States
Duration: Apr 10 2016Apr 14 2016

Other

Other35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016
CountryUnited States
CitySan Francisco
Period4/10/164/14/16

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ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Zhu, K., & Ying, L. (2016). Information source detection in networks: Possibility and impossibility results. In IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications (Vol. 2016-July). [7524363] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2016.7524363

Information source detection in networks : Possibility and impossibility results. / Zhu, Kai; Ying, Lei.

IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016. 7524363.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, K & Ying, L 2016, Information source detection in networks: Possibility and impossibility results. in IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. vol. 2016-July, 7524363, Institute of Electrical and Electronics Engineers Inc., 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, San Francisco, United States, 4/10/16. https://doi.org/10.1109/INFOCOM.2016.7524363
Zhu K, Ying L. Information source detection in networks: Possibility and impossibility results. In IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July. Institute of Electrical and Electronics Engineers Inc. 2016. 7524363 https://doi.org/10.1109/INFOCOM.2016.7524363
Zhu, Kai ; Ying, Lei. / Information source detection in networks : Possibility and impossibility results. IEEE INFOCOM 2016 - 35th Annual IEEE International Conference on Computer Communications. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016.
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