Latent Community Analysis: PhD Proposal

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wi, j = F({topic})
Topic Oriented User Communities

Topic Oriented User Communities

Topic Relatedness

Social
Issues
Entertainment
Culture
Biz
Law
Crime
Politics
Topic Relatedness

TTopic Relationship Evolution
Users Correlation
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Latent Community Analysis

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Predicting future successful teams of experts who can effectively collaborate is challenging due to the experts’ temporality of skill sets, levels of expertise, and collaboration ties, which is overlooked by prior work. Specifically, state-of-the-art neural-based methods learn vector representations of experts and skills in a static latent space, falling short of incorporating the possible drift and variability of experts’ skills and collaboration ties in time. In this paper, we propose (1) a streaming-based training strategy for neural models to capture the evolution of experts’ skills and collaboration ties over time and (2) to consume time information as an additional signal to the model for predicting future successful teams. We empirically benchmark our proposed method against state-of-the-art neural team formation methods and a strong temporal recommender system on datasets from varying domains with distinct distributions of skills and experts in teams. The results demonstrate neural models that utilize our proposed training strategy excel at efficacy in terms of classification and information retrieval metrics. The codebase is available at https://github.com/fani-lab/OpeNTF/tree/ecir24.

ECIR23: A Streaming Approach to Neural Team Formation Training

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Mining security events helps with better precautionary planning for community safety. However, incident records are expressed in diverse and application dependent formats which impedes common comprehension for automatic knowledge extraction and reasoning. In this paper, we present Security Incident Ontology, SIO, a novel light-weight domain ontology for security incidents. We use Timeline to annotate the temporal facts of incidents and adopt Event to represent any security issues from indecent behavior to assault to more adverse crime which raises the security alarm in a community. It will present a unique way to the security incident detectors, a police officer, Robocops, or intelligent CCTV cameras, to report security events. We use SIO in populating security incident notifications of Integrated Risk Management (IRM) at Ryerson University to evaluate its competency, for Ryerson University campus has both business and housing area in the vicinity and encompass not only high rate, but also a wide variety of different security issues. SIO is developed in OWL 2 with Protégé.

SEKE15: An ontology for describing security events

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We propose a temporal latent space model for user community prediction in social networks, whose goal is to predict future emerging user communities based on past history of users’ topics of interest. Our model assumes that each user lies within an unobserved latent space, and similar users in the latent space representation are more likely to be members of the same user community. The model allows each user to adjust its location in the latent space as her topics of interest evolve over time. Empirically, we demonstrate that our model, when evaluated on a Twitter dataset, outperforms existing approaches under two application scenarios, namely news recommendation and user prediction on a host of metrics such as mrr, ndcg as well as precision and f-measure.

ECIR20: Temporal Latent Space Modeling for Community Prediction

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We propose a neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest. Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis), users’ topics of interest expressed in their posted contents (content analysis), or in tandem. In such communities, however, the users’ rich temporal behavior towards topics of interest is overlooked. Only few recent research efforts consider the time dimension and define like-minded user communities as groups of users who share not only similar topical interests but also similar temporal behavior. Temporal like-minded user communities find application in areas such as recommender systems where relevant items are recommended to the users at the right time. In this paper, we tackle the problem of identifying temporally like-minded user communities by leveraging unsupervised feature learning (embeddings). Specifically, we learn a mapping from the user space to a low-dimensional vector space of features that incorporate both topics of interest and their temporal nature. We demonstrate the efficacy of our proposed approach on a Twitter dataset in the context of three applications: news recommendation, user prediction and community selection, where our work is able to outperform the state-of-the-art on important information retrieval metrics.

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To stand out from the crowd, sellers employ creative, sometimes disruptive titles for their products in online stores to improve their search relevancy or attract the attention of customers. As a part of the CIKM AnalytiCup 2017, the challenge is to build a product title quality model that can automatically grade the clarity and the conciseness of a product title. Our proposed “Bagging Model for Product Title Quality with Noise” could leave others behind in performance and become the winner of the CIKM Cup 2017 competition.

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Latent Community Analysis: PhD Proposal

1.

2.

3.

4. Social Issues Entertainment Culture Biz Law Crime Politics Social Issues Biz Law Crime Social Issues Politics Biz Law Crimes Entertainment Culture @johnpgatta[389] [1.0, 0.925,0.3, 0.532] [0.2] [0.41, 0.2] [0.3, 0.532] [0.3, 0.532, 0.6] @roylantu[29] [1.0, 0.32] [1.0, 1.0,0.8, 0.532] [0.3, 0.532] [1.0, 0.925,0.3, 0.532] [0.3]

5.

6. wi, j = F({topic}) Topic Oriented User Communities

7. Topic Oriented User Communities

8. Topic Relatedness

9. Social Issues Entertainment Culture Biz Law Crime Politics Topic Relatedness

10. TTopic Relationship Evolution Users Correlation rend

11.

12. Latent Community Analysis