Dimitrios Vogiatzis

Expert and community based style advice

We propose a knowledge framework for garment recommendations, which is based on two pillars. The first pillar incorporates knowledge about aspects of fashion, such as materials, garments, colours, body types, facial features, social occasion etc., as well as their interrelations, with the purpose of providing personalised recommendations. The said knowledge is encoded in the form of an owl ontology, the origin of which is attributed to fashion experts. Moreover, in commercial fashion sites, customers purchase garments of various types. Because of that, interesting patterns in their purchase behaviour can be sought, and thus groups of garments that tend to be purchased together can be discovered. This forms the second pillar, that can be used to enhance the first pillar with community based garment recommendations. This paper is the description and integration of the aforementioned pillars in a knowledge framework.

Determining Influential Users with Supervised Random Walks

The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.

Predicting the Evolution of Communities in Social Networks

During the last years, there is increasing interest in analyzing social networks and modeling their dynamics at different scales. This work focuses on predicting the future form of communities, which represent the mesoscale structure of networks, while the communities arise as a result of user interaction. We employ several structural and temporal features to represent communities, along with their past form, that are used to formulate a supervised learning task to predict whether a community will continue as currently is, shrink, grow or completely disappear. To test our methodology, we created a real-life social network dataset consisting of an excerpt of posts from the Mathematics Stack Exchange Q&A site. In the experiments, special care is taken in handling the class imbalance in the dataset and in investigating how the past evolutions of a community affect predictions.

Influential Users in Social Networks

A study of the influential users in online social networks is the focus of this work. Social networks expand both in terms of membership and diversity. User driven content creation is growing, and yet this information potential remains largely untapped. Future search engines focusing in social networks should take into account both the content and the structural properties of the nodes.Whereas a social network bears a superficial similarity to the Web, it is different in the sense that it connects primarily individuals rather than pages of content. Not all individuals are equally important for any given task, therefore the influential ones should be detected, in that vein we review facets of influence in social networks.

Expert based prediction of user preferences

Two influential strands in Recommender systems (RS) are the collaborative filtering and content based filtering that by taking into account user communities or interaction history suggest to the active user interesting items. However, the aforementioned approaches do not work well when confronted with new users with few interactions; or with the addition of new items. In such cases, the guidance of an expert could help the active user. In this paper we provide a definition of expert users that can be reduced into two components the expertise and the contribution. The former is related to the content of items evaluated by an expert and the latter refers to the influence of the expert to the users of a RS. In particular, contribution is learnt with the aid of a perceptron. Experts users are defined for values of the features of the items. Furthermore, we have studied the temporal evolution of the experts, as new users, new items, or new item evaluations are added into the system. Moreover, we have compared the proposed expert based method with a stereotype based method, since for both methods a minimal interaction of the active user with the RS suffices. The data originated from the MovieLens set with enhancements from the IMDB.

Predicting the evolution of communities in social networks using structural and temporal features

During the last years, there is increasing interest in analyzing social networks and modeling their dynamics at different scales. This work focuses on predicting the future form of communities, which represent the mesoscale structure of networks, while the communities arise as a result of user interaction. We employ several structural and temporal features to represent communities, along with their past form, that are used to formulate a supervised learning task to predict whether a community will continue as currently is, shrink, grow or completely disappear. To test our methodology, we created a real-life social network dataset consisting of an excerpt of posts from the Mathematics Stack Exchange Q&A site. In the experiments, special care is taken in handling the class imbalance in the dataset and in investigating how the past evolutions of a community affect predictions.

User Community Discovery

This book redefines community discovery in the new world of Online Social Networks and Web 2.0 applications, through real-world problems and applications in the context of the Web, pointing out the current and future challenges of the field. Particular emphasis is placed on the issues of community representation, efficiency and scalability, detection of communities in hypergraphs, such as multi-mode and multi-relational networks, characterization of social media communities and online privacy aspects of online communities. User Community Discovery is for computer scientists, data scientists, social scientists and complex systems researchers, as well as students within these disciplines, while the connections to real-world problem settings and applications makes the book appealing for engineers and practitioners in the industry, in particular those interested in the highly attractive fields of data science and big data analytics.

Similarity-Based User Identification Across Social Networks

In this paper we study the identifiability of users across social networks, with a trainable combination of different similarity metrics. This application is becoming particularly interesting as the number and variety of social networks increase and the presence of individuals in multiple networks is becoming commonplace. Motivated by the need to verify information that appears in social networks, as addressed by the research project REVEAL, the presence of individuals in different networks provides an interesting opportunity: we can use information from one network to verify information that appears in another. In order to achieve this, we need to identify users across networks. We approach this problem by a combination of similarity measures that take into account the users’ affiliation, location, professional interests and past experience, as stated in the different networks. We experimented with a variety of combination approaches, ranging from simple averaging to trained hybrid models. Our experiments show that, under certain conditions, identification is possible with sufficiently high accuracy to support the goal of verification.

A Parallel Algorithm for Tracking Dynamic Communities based on Apache Flink

Real world social networks are highly dynamic environments consisting of numerous users and communities, rendering the tracking of their evolution a challenging problem. In this work, we propose a parallel algorithm for tracking dynamic communities between consecutive timeframes of the social network, where communities are represented as undirected graphs. Our method compares the communities based on the widely adopted Jaccard similarity measure and is implemented on top of Apache Flink, a novel framework for parallel and distributed data processing. We evaluate the benefits, in terms of execution time, that parallel processing brings to community tracking on datasets carrying different quantitative characteristics, derived from two popular social media platforms; Twitter and Mathematics Stack Exchange Q&A. Experiments show that our parallel method has the ability to calculate the similarity of communities within seconds, even for large social networks, consisting of more than 600 communities per timeframe.

A density based algorithm for community detection in hyper-networks

We propose an efficient community detection algorithm for networks that comprise more than one entities, such as users, tags and items, with ternary or higher relations between them. Such networks are also known as multi-partite and can be used for representing social tagging systems but also the activity in streaming media. Detecting communities in multi-paritite networks entails different challenges than in simple networks. The proposed algorithm is able to detect crisp or overlapping communities, and is applied on four data sets from social tagging systems and Twitter, and is compared with other multi-partite community detection algorithms.