December 2017 - Dm-meeting - mailman.cs.vt.edu

Thursday, Dec. 14th, @ 11:00 am, Torg 3160 A, Liangzhe and Bijaya
by Sorour Ekhtiari Amiri 11 Dec '17

11 Dec '17

Hi everyone, Please join us for our next DM-Meeting. ****DM-Meeting Fall 2017**** WHO: Liangzhe Chen and Bijaya Adhikari WHEN: Thursday, Dec. 14th, 2017, @ 11:00 am WHERE: Torg 3160 A WHAT: Liangzhe and Bijaya will present the following papers, Title: Topological Recurrent Neural Network for Diffusion Prediction Abstract: In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence of nodes ordered by their activation time stamps; instead, it has a richer structure indicating the diffusion process over the data graph. As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure. We find it challenging to model diffusion topologies, which are dynamic directed acyclic graphs (DAGs), with the existing neural networks. Therefore, we propose a novel topological recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We customize Topo-LSTM for the diffusion prediction task, and show it improves the state-of-theart baselines, by 20.1%–56.6% (MAP) relatively, across multiple real-world data sets. Our code and data sets are available online Title: The Co-Evolution Model for Social Network Evolving and Opinion Migration Abstract: Almost all real-world social networks are dynamic and evolving with time, where new links may form and old links may drop, largely determined by the homophily of social actors (i.e., nodes in the network). Meanwhile, (latent) properties of social actors, such as their opinions, are changing along the time, partially due to social influence received from the network, which will in turn affect the network structure. Social network evolution and node property migration are usually treated as two orthogonal problems, and have been studied separately. In this paper, we propose a co-evolution model that closes the loop by modeling the two phenomena together, which contains two major components: (1) a network generative model when the node property is known; and (2) a property migration model when the social network structure is known. Simulation shows that our model has several nice properties: (1) it can model a broad range of phenomena such as opinion convergence (i.e., herding) and community-based opinion divergence; and (2) it allows us control the evolution via a set of factors such as social influence scope, opinion leader, and noise level. Finally, the usefulness of our model is demonstrated by an application of co-sponsorship prediction for legislative bills in Congress, which outperforms several state-of-the-art baselines. Best regards, Sorour

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Thursday, Dec. 7th, @ 11:00 am, Torg 3160 A, Aman Ahuja
by Sorour Ekhtiari Amiri 04 Dec '17

04 Dec '17

Hi everyone, Please join us for our next DM-Meeting. ****DM-Meeting Fall 2017**** WHO: Aman Ahuja WHEN: Thursday, Dec. 7th, 2017, @ 11:00 am WHERE: Torg 3160 A WHAT: Aman will present his ICDM 2017 paper. Title: A Probabilistic Geographical Aspect-Opinion Model for Geo-tagged Microblogs Abstract: Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods. Best regards, Sorour

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Thursday, Nov 30th, @ 11:00 am, Torg 3160 A, Xinfeng Xu
by Sorour Ekhtiari Amiri 30 Nov '17

30 Nov '17

Hi everyone, Please join us for our next DM-Meeting. ****DM-Meeting Fall 2017**** WHO: Xinfeng Xu WHEN: Thursday, Nov. 30th, 2017, @ 11:00 am WHERE: Torg 3160 A WHAT: Xinfeng will present the following papers, 1. EmbedJoin: Efficient Edit Similarity Joins via Embeddings: Haoyu Zhang, Qin Zhang, KDD17 Abstract: We study the problem of edit similarity joins, where given a set of strings and a threshold value K, we want to output all pairs of strings whose edit distances are at most K. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds. 2. Efficient Correlated Topic Modeling with Topic Embedding, Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing, KDD17 Abstract: Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval. Best regards, Sorour

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