TY - JOUR
T1 - Heterogeneous anomaly detection in social diffusion with discriminative feature discovery
AU - Liu, Siyuan
AU - Qu, Qiang
AU - Wang, Shuhui
N1 - Funding Information:
This research was partially supported by CAS Pioneer Hundred Talents Program, MOE Key Laboratory of Machine Perception at Peking University under grant number K-2017-02 , and the National Natural Science Foundation of China (NSFC) under grant numbers 61672497 , 61572488 and 61673241 .
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/5
Y1 - 2018/5
N2 - Social diffusion is a dynamic process of information propagation within social networks. In this paper, we study social diffusion from the perspective of discriminative features, a set of features differentiating the behaviors of social network users. We propose a new parameter-free framework based on modeling and interpreting of discriminative features that we have created, named HADISD. It utilizes a probability-distribution-based parameter-free method to identify the maximum vertex set with specified features. Using the maximum vertext set, a probability-distribution-based optimization approach is applied to find the minimum number of vertices in each feature category with the maximum discriminative information. HADISD includes an incremental algorithm to update the discriminative vertex set over time. The proposed model is capable of addressing anomaly detection in social diffusion, and the results can be leveraged for both spammer detection and influence maximization. The findings from our extensive experiments on four real-life datasets show the efficiency and effectiveness of the proposed scheme.
AB - Social diffusion is a dynamic process of information propagation within social networks. In this paper, we study social diffusion from the perspective of discriminative features, a set of features differentiating the behaviors of social network users. We propose a new parameter-free framework based on modeling and interpreting of discriminative features that we have created, named HADISD. It utilizes a probability-distribution-based parameter-free method to identify the maximum vertex set with specified features. Using the maximum vertext set, a probability-distribution-based optimization approach is applied to find the minimum number of vertices in each feature category with the maximum discriminative information. HADISD includes an incremental algorithm to update the discriminative vertex set over time. The proposed model is capable of addressing anomaly detection in social diffusion, and the results can be leveraged for both spammer detection and influence maximization. The findings from our extensive experiments on four real-life datasets show the efficiency and effectiveness of the proposed scheme.
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U2 - 10.1016/j.ins.2018.01.044
DO - 10.1016/j.ins.2018.01.044
M3 - Article
AN - SCOPUS:85041550114
SN - 0020-0255
VL - 439-440
SP - 1
EP - 18
JO - Information Sciences
JF - Information Sciences
ER -