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Wei-Xue Lu

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AAAI Conference 2016 Conference Paper

On the Minimum Differentially Resolving Set Problem for Diffusion Source Inference in Networks

  • Chuan Zhou
  • Wei-Xue Lu
  • Peng Zhang
  • Jia Wu
  • Yue Hu
  • Li Guo

In this paper we theoretically study the minimum Differentially Resolving Set (DRS) problem derived from the classical sensor placement optimization problem in network source locating. A DRS of a graph G = (V, E) is defined as a subset S ⊆ V where any two elements in V can be distinguished by their different differential characteristic sets defined on S. The minimum DRS problem aims to find a DRS S in the graph G with minimum total weight v∈S w(v). In this paper we establish a group of Integer Linear Programming (ILP) models as the solution. By the weighted set cover theory, we propose an approximation algorithm with the Θ(ln n) approximability for the minimum DRS problem on general graphs, where n is the graph size.

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IJCAI Conference 2015 Conference Paper

Influence Maximization in Big Networks: An Incremental Algorithm for Streaming Subgraph Influence Spread Estimation

  • Wei-Xue Lu
  • Peng Zhang
  • Chuan Zhou
  • Chunyi Liu
  • Li Gao

Influence maximization plays a key role in social network viral marketing. Although the problem has been widely studied, it is still challenging to estimate influence spread in big networks with hundreds of millions of nodes. Existing heuristic algorithms and greedy algorithms incur heavy computation cost in big networks and are incapable of processing dynamic network structures. In this paper, we propose an incremental algorithm for influence spread estimation in big networks. The incremental algorithm breaks down big networks into small subgraphs and continuously estimate influence spread on these subgraphs as data streams. The challenge of the incremental algorithm is that subgraphs derived from a big network are not independent and MC simulations on each subgraph (defined as snapshots) may conflict with each other. In this paper, we assume that different combinations of MC simulations on subgraphs generate independent samples. In so doing, the incremental algorithm on streaming subgraphs can estimate influence spread with fewer simulations. Experimental results demonstrate the performance of the proposed algorithm.

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