Example: Academic Search Cross-domain Ranking via Latent Space Learning Jie Tang+ and Wendy Hall▫ +Tsinghua University, ▫University of Southampton Cross-domain Ranking: learning to rank objects from multiple interrelated domains. Also called heterogeneous cross-domain ranking, when the domains are different. Example: Academic Search Input: Given T domains, with each having a training dataset—i.e., ranking pairs {(xt1q, yt1q), …, (xtnq, ytnq)}, where xt1q is K-dimensional feature vector to represent the relevance between an object in domain t and the query q at time t; ytnq ∈{r1, …, rlt}represents the ranking level of the object w.r.t. the query q; Learning: learn T ranking functions {ft}Tt=1 simultaneously, with each ft for predicting the rank level of unlabeled objects in the corresponding domain. Good morning, everyone. It’s my honor to introduce our paper “StructInf: mining structural influence from social streams”. In this paper, we study the structure characteristics of social influence. The first question we want to ask is : “in which structures, the target nodes, i.e., the write nodes in the figures, are most likely to be activated?” We take an example of retweet behavior of sina weibo in this figure. The red node represents a friend who retweets a message before time t ; the white node denotes the target user to be studied. The general question is how likely it is that the target user will retweet this message in a short time interval, conditioned on different influence structures. We find several interesting patterns from this retweet dataset. First, the conditional probability in the second figure increases to 150% higher than that of the first figure, suggesting more active friends can improve the retweet likelihood. On the other hand, the probability in the third figure increases to 300% higher than that of the second figure. Please note that the difference between figure two and three is the relationship between the friends. So our target is to mine the significant influence structures hidden in the huge volume of streaming behavior data.

Basic model The proposed model Dataset: LETOR 2.0 (Liu et al. 2007): a public dataset for learning to rank research. LETOR consists of three homogeneous sub datasets (i.e., TREC2003, TREC2004, and OHSUMED), with 50, 75, and 106 queries, respectively. AMiner (Tang et al. 2008): The dataset contains 14,134 authors, 10,716 papers, and 1,434 conferences. Given a query, the goal is to find experts, top conferences, and authoritative papers for the query. The second question is how to quickly mine those influential structures? Because to mine the influential structures from large social network, the exact algorithm need to build a diffusion graph from the input network and streaming actions, and enumerate all possible structures from each target action, and thus the time complexity is high. So We propose three different sampling algorithms to mine influential structures. The first one randomly sample nodes when enumerating influence patterns, the second one randomly reserve edges when building diffusion graph, and the third one combine the two methods. We can prove them to be unbiased. In the figure, we show the exact influence probabilities estimated by the exact algorithm and approximate influence probabilities estimated by one sampling algorithm. The results show that the proposed sampling algorithm can achieve a 10 speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. In conclusion, we present the concept of structural influence and propose efficient sampling algorithms to quickly estimate the influence probabilities of different structures from social stream. Welcome to our poster in the evening. Thanks.