Yunjia ZHANG

Yunjia ZHANG

Ph.D. student at University of Wisconsin-Madison

Publications

From Appearances to Essence: Learning Diffusion Networks from Final Infection Results (UNDER REVIEW)

Published in IJCAI (UNDER REVIEW), 2019

To infer the structures of diffusion networks, most of the existing approaches require not only the final infection statuses of the nodes in the network, but also the temporal information that pinpoints the exact time when infections occur. Nonetheless, in many natural diffusion processes such as the spread of epidemics, monitoring the exact infection time is usually difficult and expensive. In this paper, we investigate the problem of inferring the diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing the sequence of infection time to determine the potential parent-child influence relationships between the nodes, we present a conditional entropy-based method to infer the candidate parent nodes for each node in the network. Furthermore, to refine the inferred diffusion network structure, we disqualify the insignificant candidate parent nodes whose infections have very weak correlations with the infections of the corresponding child nodes. Experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach.

Inferring Diffusion Networks without Timestamps (UNDER REVIEW)

Published in ICDE (UNDER REVIEW), 2018

To infer the structures of diffusion networks, most existing approaches require not only the final infection statuses of network nodes, but also the timestamps that pinpoint the exact time when infections of the nodes occur. Nonetheless, in many natural diffusion processes such as the spread of epidemics, monitoring the exact infection timestamps is often difficult and expensive. In this work, we investigate the problem of inferring the diffusion network structures without infection timestamps, using only the final infection statuses of nodes. Instead of utilizing the sequence of timestamps to determine the potential parent-child influence relationships between the nodes, we design a probabilistic generative model of the final infection statuses to quantitatively measure the likelihood of potential structures of the objective diffusion network, taking into account network complexity. Then, for each node in the network, we can infer an appropriate number of most probable parent nodes. Furthermore, in order to reduce redundant computation during the inference, we preclude a few insignificant candidate parent nodes from being considered in the inference, if their infections have very weak correlations with the infections of the corresponding child nodes. Extensive experiments on both synthetic and real-world networks are conducted, and the results verify the effectiveness and efficiency of our approach.

Fast Inference of Diffusion Networks without Infection Temporal Information

Published in NDBC, 2018

The communication and influence among people form complex diffusion networks which consider person as nodes. Diffusion network inference can help us understand these diffusion networks and the related diffusion processes more intuitively and accurately, so as to better predict, promote or prevent future diffusion events. Most of the existing network inference algorithms require the infection temporal information of nodes. But in many real-world diffusion processes, the exact infection temporal information is often unavailable, resulting in a limited availability for existing methods. This paper aims at an effective, efficient and infection temporal information-free approach for diffusion network inference, and studies how to infer the influence relationships and transmission rates between the nodes based on only the final infection statuses of the nodes observed in a number of diffusion processes. To this end, the approach proposed in this paper first utilizes the mutual information of infection statuses among the nodes to quantify the correlation of the infections and reveal the underlying influence relationships between the nodes. Then, the proposed approach constructs the log-likelihood function of the observed infection statuses with transmission rates as the variables, and adopts the Expectation-Maximization algorithm to maximize the function and approximate the optimal transmission rates. Extensive experimental results demonstrate that compared against the existing approaches, the proposed approach has a reasonably better accuracy performance on diffusion network inference, and significantly reduces runtime.

Discussions about The Application and Analysis of Computer Network on E-Commerce

Published in Digital Communication World, 2015

At present, e-commerce has become the main shopping method for young people, but e-commerce in its development shows various problems, especially network security. Therefore, computer network technology is particularly important. Based on the application status of computer network technology in e-commerce, this paper analyzes the computer network technology commonly used in e-commerce. Starting from the actual situation, we study the problems of the use of e-commerce in computer network technology, and put forward feasible strategies to contribute to the application of computer network technology.