April 2019 - Dm-meeting - mailman.cs.vt.edu

Monday, May 6th, 2019 - 11 AM @ McBryde 133C, Bijaya Adhikari
by Alexander Rodriguez 05 May '19

05 May '19

Please join us for our next DM-Meeting. ****DM-Meeting Spring 2019**** WHO: Bijaya Adhikari WHEN: Monday, May 6th, 2019 - 11 AM WHERE: McBryde 133C WHAT: Bijaya will practice his preliminary proposal "" Title: Domain-based Frameworks and Embeddings for Dynamics over Networks Abstract: Broadly this thesis looks into network and time-series mining problems pertaining to dynamics over networks in various domains. We answer questions like which locations and staff should we monitor in order to detect *C. Difficle* outbreaks in hospitals as quickly as possible? How do we predict the peak intensity of the influenza incidence in the current season in an interpretable fashion? How do we select the group of queries which are most critical to ensure high user satisfaction for an E-commerce system? Due to ease of data collection in various domains, it is now possible to answer these questions. However, these questions pose several new challenges. The first challenge is to (a) incorporate more realistic dynamics. The dynamical processes in the domains we consider are complicated. For example, *C. Difficle* spreads via both people-to-people and surface-to-people interactions and in E-commerce users can decide to continue interaction with the system or arbitrarily decide to stop and move on. Traditional transmission models like SI, IC, and SIR are too restrictive to capture these processes. Hence, we need to incorporate more realistic dynamics into account to accurately answer the questions above. The second challenge is to (b) handle data sparsity. Despite data collection being easier and cheaper, data sparsity problems persist. Specially, the data related to incidence like infections and node failures in infrastructure networks are difficult to obtain as they are constrained by actual events and are hence hard to observe and directly sample from. The third challenge is to (c) ensure robust performance. For the questions presented above, the existing approaches are based on heuristics and fail to provide robust guarantees in performance. However, robustness is of much importance for the critical problems we study. In this thesis, we propose to leverage domain-based frameworks, which includes novel models and analysis techniques, and domain-based low dimensional representation learning to tackle the challenges above for networks and time-series mining tasks. The first challenge we must overcome is to incorporate realistic transmission models to capture the complicated dynamics. However, more realistic transmission model often requires more challenging analysis techniques in formulating problems and designing algorithms. Hence, we propose to leverage domain-based frameworks, including both the models and analysis techniques to answer tackle these problems. For example, for *C. Difficle* outbreak detection problem, we use two-mode disease model to capture both people-to-people and locations-to-people infection pathways of *C. Difficle* and discrete lattice based optimization framework. Similarly, we propose randomized user navigation model along with submodular function optimization for critical queries mining in e-commerce and leverage geographically correlated failure models with Minimum Description Length formulation to infer network states. We overcome the second challenge on data sparsity by leveraging novel models as well as by including domain-aware low dimensional embeddings. As we use more realistic models to accurately capture the dynamics, we can generalize from the sparse data better, hence overcoming the data sparsity problem. On the other hand, domain-aware embeddings capture the important domain-based aspect of the data and can help guide the learning architecture to infer the correct function with less data. We carefully construct domain-based frameworks by incorporating realistic models and most relevant analysis techniques necessitated by the model. This approach leads to meaningful problem formulation and often leads to near optimal algorithms. On the other hand, domain-based embeddings augment learning architectures and improve the performance. Our thesis has several applications in critical tasks in variety of domains. For example, our domain-aware frameworks for *C. Difficle*allows different monitoring rates for people and locations, which the prior works fail to do. Hence, our approach can detect more than 95% of outbreaks, which is greater than any other competitor. Similarly, our framework for product recommendation in E-commerce for queries with sparse engagement data results in 34% improvement over Walmart.com search engine. It also helped us in detecting most critical queries in e-commerce outperforming non-trivial baselines like page-ranks, most frequent queries, and so on. Similarly, we propose near optimal algorithms for inferring network states given partial observation of the failures in networks. On the other hand, by exploiting domain aware low dimensional representations, we outperform non-trivial competitors by up to 40 percentage for influenza forecasting. Similarly, domain-aware representations of subgraphs helped us outperform non-trivial baselines in the graph classification task. Best regards, *Alexander Rodriguez* PhD Student Department of Computer Science Virginia Tech _______________________________________________ Seminar website: *http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html <http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html>* Please contact Alexander Rodriguez (arodriguez(a)cs.vt.edu <esorour(a)cs.vt.edu>), if interested in giving a talk. Dm-meeting mailing list Dm-meeting(a)cs.vt.edu https://mailman.cs.vt.edu/mailman/listinfo/dm-meeting

1 1

Congratulations to Sorour and Xinfeng
by B. Aditya Prakash 26 Apr '19

26 Apr '19

Congratulations Dr. Amiri, a great job at the defense! Congratulations to Xinfeng for both finishing up his MS in CS, and also winning the department's best MS thesis award! (award will be given next week on May 1 during the dept's annual function, I will miss it, others in town should try to go!) Thanks to everyone who helped with the numerous practice talks :-) cheers, - Aditya

3 2

Tuesday, April 30th @ 1 PM, Tor 3160A, Bijaya Adhikari
by Alexander Rodriguez 21 Apr '19

21 Apr '19

Please join us for our next DM-Meeting. ****DM-Meeting Spring 2019**** WHO: Bijaya Adhikari WHEN: Tuesday, April 30th @ 1 PM WHERE: Tor 3160A WHAT: Bijaya will practice his SDM talk "" Title: Joint Post and Link-level Influence Modeling on Social Media Abstract: Microblogging websites, like Twitter and Weibo, are used by billions of people to create and spread information. This activity depends on various factors such as the friendship links between users, their topic interests and social influence between them. Social influence can be thought of as a latent factor, that may alter users posting and linking behaviors. Making sense of these behaviors is very important for fully understanding and utilizing these platforms. Most prior work in this space either ignores the effect of social influence, or considers its effect only on link formation or post generation. In contrast, we propose PoLIM, leveraging simple weak supervision, a novel model which jointly models the effect of influence on both link and post generation. We also give PoLIM-FIT, an efficient parallel inference algorithm which scales to large datasets. In our experiments on a large tweets corpus, we detect meaningful topical communities, celebrities, as well as the influence strengths patterns among them. Further, we find that there are significant portions of posts and links that are caused by influence, and this portion increases when the data focuses on a specific event. We also show that differentiating and identifying these influenced content benefits other specific quantitative downstream tasks as well, like predicting future tweets and link formation, where we significantly outperform state-of-the-art. Best regards, *Alexander Rodriguez* PhD Student Department of Computer Science Virginia Tech _______________________________________________ Seminar website: *http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html <http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html>* Please contact Alexander Rodriguez (arodriguez(a)cs.vt.edu <esorour(a)cs.vt.edu>), if interested in giving a talk. Dm-meeting mailing list Dm-meeting(a)cs.vt.edu https://mailman.cs.vt.edu/mailman/listinfo/dm-meeting

1 0

Tuesday, April 30th @ 1 PM, Tor 3160A, Bijaya Adhikari
by Alexander Rodriguez 21 Apr '19

21 Apr '19

Please join us for our next DM-Meeting. ****DM-Meeting Spring 2019**** WHO: Bijaya Adhikari WHEN: Tuesday, April 30th @ 1 PM WHERE: Tor 3160A WHAT: Bijaya will practice his SDM talk "" Title: Joint Post and Link-level Influence Modeling on Social Media Abstract: Microblogging websites, like Twitter and Weibo, are used by billions of people to create and spread information. This activity depends on various factors such as the friendship links between users, their topic interests and social influence between them. Social influence can be thought of as a latent factor, that may alter users posting and linking behaviors. Making sense of these behaviors is very important for fully understanding and utilizing these platforms. Most prior work in this space either ignores the effect of social influence, or considers its effect only on link formation or post generation. In contrast, we propose PoLIM, leveraging simple weak supervision, a novel model which jointly models the effect of influence on both link and post generation. We also give PoLIM-FIT, an efficient parallel inference algorithm which scales to large datasets. In our experiments on a large tweets corpus, we detect meaningful topical communities, celebrities, as well as the influence strengths patterns among them. Further, we find that there are significant portions of posts and links that are caused by influence, and this portion increases when the data focuses on a specific event. We also show that differentiating and identifying these influenced content benefits other specific quantitative downstream tasks as well, like predicting future tweets and link formation, where we significantly outperform state-of-the-art. Best regards, *Alexander Rodriguez* PhD Student Department of Computer Science Virginia Tech _______________________________________________ Seminar website: *http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html <http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html>* Please contact Alexander Rodriguez (arodriguez(a)cs.vt.edu <esorour(a)cs.vt.edu>), if interested in giving a talk. Dm-meeting mailing list Dm-meeting(a)cs.vt.edu https://mailman.cs.vt.edu/mailman/listinfo/dm-meeting

1 0

Monday, April 22th @ 11 AM, McB 133C, Sorour Amiri
by Alexander Rodriguez 16 Apr '19

16 Apr '19

Please join us for our next DM-Meeting. ****DM-Meeting Spring 2019**** WHO: Sorour Amiri WHEN: Monday, April 22th @ 11 AM WHERE: McB 133C WHAT: Sorour will practice her PhD defense "" Title: Task-specific Summarization of Networks: Optimization and Learning Abstract: Networks (also known as graphs) are everywhere. People-contact networks, social networks, email communication networks, internet networks (among others) are examples of graphs in our daily life. The increasing size of these networks and simplicity to collect data diminishes our understanding of patterns in data. It is essential and at the same time challenging to analyze and understand the networks. Consider a social network such as Facebook or Twitter. A graph can represent the friendship between users. If we take the profile information or the strength of the friendship of users into account, we can have a weighted attributed graph. Also, we can consider the information associated with time stamps and produce a temporal friendship network. Mining and summarizing these graphs is an important task which reveals key patterns in the networks and helps in many applications such as visualizing and highlighting different communities or influential users in the network. In addition, summarizing networks helps in accelerating existing graph algorithms. We can run expensive algorithms such as ``finding influential users" or ``generating high-quality graph layouts" on smaller summary graphs and map the results to the original graph later. Finally, summarizing networks provides a high-level abstraction of them and helps in making generalized methods for a set of similar graphs. It is notable that different summarizes are desired for different purposes. For example, to stop viral infections, one may want to find an effective policy to immunize people in a people-contact network. In this case, a high-quality network summary should highlight the best people to immunize. Others may want to detect communities in the same people-contact network. Hence, we need another summary to show the communities. This means that for each task, we must design a specific method to reveal the related patterns. Despite the importance of summarizing networks for particular tasks, there has not been much work in this area. In this thesis, we design task-specific summarization frameworks for univariate and multivariate networks to reveal their important patterns and to accelerate existing algorithms and making sense of them. We start with optimization-based approaches to summarize graphs for a particular task and finally propose general frameworks which learn how to summarize the network for a given task and generalize it to similar networks. *Optimization-based approach:* Given a large network and a task, we propose summarization algorithms to highlight the characteristics of the graph (i.e., structure, attribute, label, dynamic) with respect to the task. We develop effective and efficient algorithms for summarizing univariate and multivariate graphs given various tasks such as content-aware influence maximization and time segmentation. In addition, we study many real-world networks and their summary graphs such as people-contact, news-blogs, etc. and visualize them to make sense of their characteristics given the input task. *Learning-based approach:* As our next step toward graph summarization, we propose a unified framework which learns the process of summarization for a given task. First, we design a generative algorithm to learn to summarize graphs for any Graph Optimization Problem (GOP). Next, we go further and add sparse human feedback to the learning process of summarization for the given optimization task. Our models and frameworks lead to meaningful discoveries. We also solve problems from various domains such as epidemiology, marketing, social media, cybersecurity, and interactive visualization. Best regards, *Alexander Rodriguez* PhD Student Department of Computer Science Virginia Tech _______________________________________________ Seminar website: *http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html <http://people.cs.vt.edu/~arodriguez/DM-Meeting/home.html>* Please contact Alexander Rodriguez (arodriguez(a)cs.vt.edu <esorour(a)cs.vt.edu>), if interested in giving a talk. Dm-meeting mailing list Dm-meeting(a)cs.vt.edu https://mailman.cs.vt.edu/mailman/listinfo/dm-meeting

1 0

PyTorch-BigGraph: Faster embeddings of large graphs
by B. Aditya Prakash 02 Apr '19

02 Apr '19

https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embe… Looks interesting! Apologies for the brevity Sent from my iPhone

1 0