TY - JOUR
T1 - Cross-dependency inference in multi-layered networks
T2 - A collaborative filtering perspective
AU - Chen, Chen
AU - Tong, Hanghang
AU - Xie, Lei
AU - Ying, Lei
AU - He, Qing
N1 - Funding Information:
This work is supported by DTRA under the grant number HDTRA1-16-0017, National Science Foundation under Grant No. IIS-1651203, Army Research Office under the contract number W911NF-16-1-0168, National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-33 25 and a Baidu gift.
PY - 2017/6
Y1 - 2017/6
N2 - The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, crossplatform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm FASCINATE that can reveal unobserved dependencies with linear complexity. Moreover, we derive FASCINATE-ZERO, an online variant of FASCINATE that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.
AB - The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, crossplatform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm FASCINATE that can reveal unobserved dependencies with linear complexity. Moreover, we derive FASCINATE-ZERO, an online variant of FASCINATE that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.
KW - Cross-layer dependency
KW - Graph mining
KW - Multi-layered network
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U2 - 10.1145/3056562
DO - 10.1145/3056562
M3 - Article
C2 - 29204108
AN - SCOPUS:85023174347
SN - 1556-4681
VL - 11
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
IS - 4
M1 - 3056562
ER -