Vishrawas Gopalakrishnan

Tracking Temporal Community Strength in Dynamic Networks

Community formation analysis of dynamic networks has been a hot topic in data mining which has attracted much attention. Recently, there are many studies which focus on discovering communities successively from consecutive snapshots by considering both the current and historical information. However, these methods cannot provide us with much historical or successive information related to the detected communities. Different from previous studies which focus on community detection in dynamic networks, we define a new problem of tracking the progression of the community strength-a novel measure that reflects the community robustness and coherence throughout the entire observation period. To achieve this goal, we propose a novel framework which formulates the problem as an optimization task. The proposed community strength analysis also provides foundation for a wide variety of related applications such as discovering how the strength of each detected community changes over the entire observation period. To demonstrate that the proposed method provides precise and meaningful evolutionary patterns of communities which are not directly obtainable from traditional methods, we perform extensive experimental studies on one synthetic and five real datasets: Social evolution, tweeting interaction, actor relationships, bibliography, and biological datasets. Experimental results show that the proposed approach is highly effective in discovering the progression of community strengths and detecting interesting communities.

Matching product titles using web-based enrichment

Matching product titles from different data feeds that refer to the same underlying product entity is a key problem in online shopping. This matching problem is challenging because titles across the feeds have diverse representations with some missing important keywords like brand and others containing extraneous keywords related to product specifications. In this paper, we propose a novel unsupervised matching algorithm that leverages web earch engines to (1) enrich product titles by adding important missing tokens that occur frequently in search results, and (2) compute importance scores for tokens based on their ability to retrieve other (enriched title) tokens in search results. Our matching scheme calculates the Cosine similarity between enriched title pairs with tokens weighted by their importance scores. We propose an optimization that exploits the templatized structure of product titles to reduce the number of search queries. In experiments with real-life shopping datasets, we found that our matching algorithm has superior F1 scores compared to IDF-based cosine similarity.

Collaborative restricted boltzmann machine for social event recommendation

The development of social networks has not only improved the online experience, but also stimulated the advances in knowledge mining so as to assist people in planning their offline social events. Users can explore their favorite events, such as celebrations and symposiums, through the pictures and the posts from their friends on social networks. An effective event recommendation can offer great convenience for both event organizers and participants, which yet remains extremely challenging due to a wide range of practical concerns. In this paper we propose a novel recommendation framework, which combines the information from multiple sources and establishes a connection between the online knowledge and the event participation.

Topic Discovery for Short Texts Using Word Embeddings

Discovering topics in short texts, such as news titles and tweets, has become an important task for many content analysis applications. However, due to the lack of rich context information in short texts, the performance of conventional topic models on short texts is usually unsatisfying. In this paper, we propose a novel topic model for short text corpus using word embeddings. Continuous space word embeddings, which is proven effective at capturing regularities in language, is incorporated into our model to provide additional semantics. Thus we model each short document as a Gaussian topic over word embeddings in the vector space. In addition, considering that background words in a short text are usually not semantically related, we introduce a discrete background mode over word types to complement the continuous Gaussian topics. We evaluate our model on news titles from data sources like abcnews, showing that our model is able to extract more coherent topics from short texts compared with the baseline methods and learn better topic representation for each short document.

Towards self-learning based hypotheses generation in biomedical text domain

Motivation: The overwhelming amount of research articles in the domain of bio‐medicine might cause important connections to remain unnoticed. Literature Based Discovery is a sub‐field within biomedical text mining that peruses these articles to formulate high confident hypotheses on possible connections between medical concepts. Although many alternate methodologies have been proposed over the last decade, they still suffer from scalability issues. The primary reason, apart from the dense inter‐connections between biological concepts, is the absence of information on the factors that lead to the edge‐formation. In this work, we formulate this problem as a collaborative filtering task and leverage a relatively new concept of word‐vectors to learn and mimic the implicit edge‐formation process. Along with single‐class classifier, we prune the search‐space of redundant and irrelevant hypotheses to increase the efficiency of the system and at the same time maintaining and in some cases even boosting the overall accuracy. Results: We show that our proposed framework is able to prune up to 90% of the hypotheses while still retaining high recall in top‐K results. This level of efficiency enables the discovery algorithm to look for higher‐order hypotheses, something that was infeasible until now. Furthermore, the generic formulation allows our approach to be agile to perform both open and closed discovery. We also experimentally validate that the core data‐structures upon which the system bases its decision has a high concordance with the opinion of the experts.This coupled with the ability to understand the edge formation process provides us with interpretable results without any manual intervention. Availability and implementation: The relevant JAVA codes are available at: https://github.com/vishrawas/Medline‐Code_v2. Supplementary information: Supplementary data are available at Bioinformatics online.

A Survey on Context Learning

Learning semantics based on context information has been researched in many research areas for decades. Context information can not only be directly used as the input data, but also sometimes used as auxiliary knowledge to improve existing models. This survey aims at providing a structured and comprehensive overview of the research on context learning. We summarize and group the existing literature into four categories, Explicit Analysis, Implicit Analysis, Neural Network Models, and Composite Models, based on the underlying techniques adopted by them. For each category, we talk about the basic idea and techniques, and also introduce how context information is utilized as the model input or incorporated into the model to enhance the performance or extend the domain of application as auxiliary knowledge. In addition, we discuss the advantages and disadvantages of each model from both the technical and practical point of view.

MESS: A Multilingual Error based String Similarity measure for transliterated name variants

Cross-lingual name matching is an important problem in the fields of machine translation and data mining. Though well studied, it lacks a generic solution largely due to issues like language specific nuances, resource scarcity, etc. Most of the proposed unsupervised approaches focus on a small subset of languages, mostly English and its derivatives, and employ specific handcrafted rules that do not port well to other languages. In this paper, we propose a generic multilingual solution that instead adds simple probabilistic extensions to existing string similarity methods. Not only does our solution depend only on freely available open source resources but we also demonstrate the superiority of our approach on 60 language pairs drawn across language families.

De-noise biological network from heterogeneous sources via link propagation

Lots of recent bioinformatics works have focused on the inference of various types of biological networks, such as gene coexpression networks, protein-protein interaction networks, signal transduction networks, etc. Unfortunately, these raw biological network data often contain much noise, especially the false positive predictions which in many cases hinder accurate reconstruction of biological networks. In addition, since the labeled data is scarce and expensive, we hope that the knowledge from other domains can help handle this lack of labeled data problem. In order to construct a more robust and reliable biological network, we propose a novel link propagation based algorithm to de-noise false positives from the target biological network through propagating information from few labeled samples and a set of auxiliary domain networks. While comparing with many current state-of-the-art algorithms, our proposed approach has shown good performance in de-noising biological network.

Influence based analysis of community consistency in dynamic networks

The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.

Significant Edge Detection in Target Network by Exploring Multiple Auxiliary Networks

Despite the ability to model many real world settings as a network, one major challenge in analyzing network data is that important and reliable links between objects are usually obscured by noisy information and hence not readily discernible. In this paper, we propose to detect these important and reliable links - significant edges, from a target network by using multiple auxiliary networks and a limited amount of labelled information. In this process, we first abstract the community knowledge learnt across target and auxiliary networks to detect significant patterns. The mined community knowledge captures the key profile of network relationships and thus can be used to determine whether an existing edge indicates a true or false relationship. Experiments on real world network data show that our two staged solution - a joint matrix factorisation procedure followed by edge significance score ranking, accurately predicts significant edges in target network by jointly exploring the underlying knowledge embedded in both target and auxiliary networks.