Yuanxing Zhang
Peking University
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Yuanxing Zhang.
international conference on communications | 2017
Chengliang Gao; Yuanxing Zhang; Kaigui Bian; Zhuojin Li; Yichong Bai; Xuanzhe Liu
Businesses are interested in marketing over the mobile social network (MSN) during holiday seasons to expand their holiday sales. Understanding the “holiday syndrome” — how people behave during holidays — over the MSN is important to create a successful marketing campaign that delights customers. In this paper, we conduct an empirical measurement study of WeChat Moments (the most popular social network of the mobile messaging app WeChat in China) during the holiday season of Chinese Spring Festival (with 137 million users involved and 329 thousand applications crawled), and present a comprehensive view of the MSNs impact on the social ties among users, user interests, as well as users migration patterns before, during, and after the holiday. Our research findings suggest that the MSN is predominantly used during holiday seasons for holiday-atmosphere building and experience sharing. It is revealed that there exist strong correlations between the timing and popular topics, and between holiday migration and regional distribution. Our results hold the promise of helping businesses to promote their marketing information dissemination to targeted groups of customers, at the right region, with appealing words, during the holiday season.
global communications conference | 2016
Yuanxing Zhang; Yichong Bai; Lin Chen; Kaigui Bian; Xiaoming Li
Many online social networks have provided a messenger app (e.g., facebook messenger, direct message on Twitter) to facilitate communication between strong- tied friends. Meanwhile, some messenger apps (WeChat) also start to offer social-networking services (WeChat Moments (WM), a.k.a. friend circle) that allow users to post pictures, texts, links of webpages, on their walls, which is called the messenger-based social network (Msg-SN). In online social networks, Key Opinion Leaders (KOLs) with millions of followers are easy to identify for helping viral marketing/advertising. However, most users of a messenger app have a small number of friends (e.g., hundreds of friends), which makes it challenging to detect a KOL in Msg-SN by only counting the number of his/her friends. In this paper, we study the influence maximization problem in the Msg-SN of finding the set of most influential KOL nodes that maximize the spread of information. We develop a novel efficient approximation algorithm that calculates the influence by looking at the users local contribution to the information diffusion process, which scales to large datasets with provable near-optimal performance. Experiment results using the real-world WeChat Moments data (on January 14th, 2016, 100 thousand users) show that our algorithms can identify the set of KOL nodes with a low time complexity.
Proceedings of the 2nd International Conference on Crowd Science and Engineering | 2017
Yuanxing Zhang; Zhuqi Li; Kaigui Bian; Yichong Bai; Zhi Yang; Xiaoming Li
Today, many applications depend on the projection on the population distribution in geographical regions, such as launching marketing campaigns and enhancing the public safety in certain densely-populated areas. Demographic and sociological researches have provided various ways of collecting peoples trajectory data through offline means. However, collecting offline data consumes a lot of resources, and the data availability is usually limited. Fortunately, the wide spread of online social network (OSN) applications over mobile devices reflect many geographical information, where we could devise a light weight approach of conducting the study on the projection of the population distribution using the online data. In this paper, we propose a geo-homophily model in OSNs to help project the population distribution in a given division of geographical regions. We establish a three-layer theoretic framework: It first describes the relationship between the online message diffusion among friends in the OSN and the offline population distribution over a given division of regions via a Dirichlet process, and then projects the floating population across the regions. Evaluations over large-scale OSN datasets show that the proposed prediction models can characterize the process of the formation of the population distribution and the changes of the floating population over time with a high prediction accuracy.
international joint conference on artificial intelligence | 2018
Yuanxing Zhang; Yangbin Zhang; Kaigui Bian; Xiaoming Li
Machine reading comprehension has gained attention from both industry and academia. It is a very challenging task that involves various domains such as language comprehension, knowledge inference, summarization, etc. Previous studies mainly focus on reading comprehension on short paragraphs, and these approaches fail to perform well on the documents. In this paper, we propose a hierarchical match attention model to instruct the machine to extract answers from a specific short span of passages for the long document reading comprehension (LDRC) task. The model takes advantages from hierarchical-LSTM to learn the paragraphlevel representation, and implements the match mechanism (i.e., quantifying the relationship between two contexts) to find the most appropriate paragraph that includes the hint of answers. Then the task can be decoupled into reading comprehension task for short paragraph, such that the answer can be produced. Experiments on the modified SQuAD dataset show that our proposed model outperforms existing reading comprehension models by at least 20% regarding exact match (EM), F1 and the proportion of identified paragraphs which are exactly the short paragraphs where the original answers locate.
International Journal of Crowd Science | 2017
Yuanxing Zhang; Zhuqi Li; Kaigui Bian; Yichong Bai; Zhi Yang; Xiaoming Li
Purpose n n n n nProjecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks. n n n n nDesign/methodology/approach n n n n nIn this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions. n n n n nFindings n n n n nBy experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time. n n n n nOriginality/value n n n n nThis paper tries to project population distribution by modeling geo-homophily in OSNs.
IEEE Network | 2018
Yuanxing Zhang; Zhuqi Li; Chengliang Gao; Kaigui Bian; Lingyang Song; Shaoling Dong; Xiaoming Li
international conference on computer communications | 2018
Yuanxing Zhang; Chengliang Gao; Yangze Guo; Kaigui Bian; Xin Jin; Zhi Yang; Lingyang Song; Jiangang Cheng; Hu Tuo; Xiaoming Li
international conference on communications | 2018
Yangze Guo; Yuanxing Zhang; Zhi Yang; Kaigui Bian; Hu Tuo; Yafei Dai
international conference on communications | 2018
Chengliang Gao; Yuanxing Zhang; Kaigui Bian; Shaoling Dong; Lingyang Song
international conference on big data | 2018
Yuanxing Zhang; Kaigui Bian; Hu Tuo; Bin Cui; Lingyang Song; Xiaoming Li