Information source detection, which is the reverse problem of information diffusion, has attracted considerable research effort recently. Most existing approaches assume that the underlying propagation model is fixed and given as input, which may limit their application range. In this paper, we study the multiple source detection problem when the underlying propagation model is unknown. Our basic idea is source prominence, namely the nodes surrounded by larger proportions of infected nodes are more likely to be infection sources. As such, we propose a multiple source detection method called Label Propagation based Source Identification (LPSI). Our method lets infection status iteratively propagate in the network as labels, and finally uses local peaks of the label propagation result as source nodes. In addition, both the convergent and iterative versions of LPSI are given. Extensive experiments are conducted on several real-world datasets to demonstrate the effectiveness of the proposed method.

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Information diffusion is one of the most important issues in social network analysis. Unlike most existing works, which either rely on network topology or node profiles, this study focuses on the diffusion itself, i.e., the recorded propagation histories. These histories are the evidence of diffusion and can be used to explain to users what happened in their networks. However, these histories can quickly grow in size and complexity, limiting their capacity to be intuitively understood. To reduce this information overload, in this paper we present the problem of propagation history ranking. The goal is to rank participant edges/nodes by their contribution to the diffusion. We first discuss and adapt a causal measure, Difference of Causal Effects (DCE), as the ranking criterion. Then, to avoid the complex calculation of DCE, we propose two integrated ranking strategies by adopting two indicators. One is responsibility, which captures the necessity aspect of causal effects. We further give an approximate algorithm, which could guarantee a feasible solution, for this indicator. The other is capability, which captures the sufficiency aspect of causal effects. Finally, promising experimental results are presented to verify the feasibility of the proposed ranking strategies.

Provenance Based Access Control (PBAC) is a new access control mechanism wherein the access control decisions are made based on a set of assertions about provenance traces. Manually designing a variety of provenance based security policies is not trivial work for big data applications with large amount of provenance entity types and complex provenance dependencies. Policy retrieval can reduce such manual labor by automatically "learning" policies from previous provenance traces. In this paper, we look into the composition of PBAC rules to determine the relevant knowledge that should be mined from provenance traces for policy retrieval. We propose a baseline retrieval approach which composes the mined knowledge into candidate rules and verifies them by feeding them into a decision-tree classifier as candidate classification features. We show the feasibility and limitations of the baseline approach with experimenting and thereby present suggestions about the future work for PBAC policy retrieval research.

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