Ilias Gialampoukidis

Age and Time Operator of Evolutionary Processes

The Time Operator and Internal Age are intrinsic features of Entropy producing Innovation Processes. The innovation spaces at each stage are the eigenspaces of the Time Operator. The internal Age is the average innovation time, analogous to lifetime computation. Time Operators were originally introduced for Quantum Systems and highly unstable Dynamical Systems. The goal of this work is to present recent extensions of Time Operator theory to regular Markov Chains and Networks in a unified way and to illustrate the Non-Commutativity of Net Operations like Selection and Filtering in the context of Knowledge Networks.

Entropy, Age and Time Operator

The time operator and internal age are intrinsic features of entropy producing innovation processes. The innovation spaces at each stage are the eigenspaces of the time operator. The internal age is the average innovation time, analogous to lifetime computation. Time operators were originally introduced for quantum systems and highly unstable dynamical systems. Extending the time operator theory to regular Markov chains allows one to relate internal age with norm distances from equilibrium. The goal of this work is to express the evolution of internal age in terms of Lyapunov functionals constructed from entropies. We selected the Boltzmann–Gibbs–Shannon entropy and more general entropy functions, namely the Tsallis entropies and the Kaniadakis entropies. Moreover, we compare the evolution of the distance of initial distributions from equilibrium to the evolution of the Lyapunov functionals constructed from norms with the evolution of Lyapunov functionals constructed from entropies. It is remarkable that the entropy functionals evolve, violating the second law of thermodynamics, while the norm functionals evolve thermodynamically.

Community detection in complex networks based on DBSCAN* and a Martingale process

Community detection is a valuable tool for analyzing and understanding the structure of complex networks. This work investigates the application of the density-based algorithm DBSCAN* to the community detection problem. Given, though, that this algorithm requires a lower bound for the community size to be determined a priori, this work proposes the application of a Martingale process to DBSCAN* so as to progressively detect communities at various levels of granularity, without the need to define in advance a single threshold for the minimum community size. In particular, the proposed DBSCAN*-Martingale community detection algorithm corresponds to an iterative process that progressively lowers the threshold of the size of the acceptable communities, while maintaining the communities detected for higher thresholds. Evaluation experiments are performed based on four realistic benchmark networks and the results indicate improvements in the effectiveness of the proposed DBSCAN*-Martingale community detection algorithm in terms of the Normalized Mutual Information and RAND metrics against several state-of-the-art community detection approaches.

Detection of Terrorism-related Twitter Communities using Centrality Scores

Social media are widely used among terrorists to communicate and disseminate their activities. User-to-user interaction (e.g. mentions, follows) leads to the formation of complex networks, with topology that reveals key-players and key-communities in the terrorism domain. Both the administrators of social media platforms and Law Enforcement Agencies seek to identify not only single users but groups of terrorism-related users so that they can reduce the impact of their information exchange efforts. To this end, we propose a novel framework that combines community detection with key-player identification to retrieve communities of terrorism-related social media users. Experiments show that most of the members of each retrieved key-community are already suspended by Twitter, violating its terms, and are hence associated with terrorism-oriented content with high probability.

Unsupervised Keyword Extraction Using the GoW Model and Centrality Scores

Nowadays, a large amount of text documents are produced on a daily basis, so we need efficient and effective access to their content. News articles, blogs and technical reports are often lengthy, so the reader needs a quick overview of the underlying content. To that end we present graph-based models for keyword extraction, in order to compare the Bag of Words model with the Graph of Words model in the keyword extraction problem. We compare their performance in two publicly available datasets using the evaluation measures Precision@10, mean Average Precision and Jaccard coefficient. The methods we have selected for comparison are grouped into two main categories. On the one hand, centrality measures on the formulated Graph-of-Words (GoW) are able to rank all words in a document from the most central to the less central, according to their score in the GoW representation. On the other hand, community detection algorithms on the GoW provide the largest community that contains the key nodes (words) in the GoW. We selected these methods as the most prominent methods to identify central nodes in a GoW model. We conclude that term-frequency scores (BoW model) are useful only in the case of less structured text, while in more structured text documents, the order of words plays a key role and graph-based models are superior to the term-frequency scores per document.

Analysis of Suspended Terrorism-Related Content on Social Media

Social media are widely used by terrorist organizations and extremist groups for disseminating propaganda and recruiting new members. Given the recent pledges both by the major social media platforms and governments towards combating online terrorism, our work aims at understanding the terrorism-related content posted on social media and distinguishing accounts of relevance to terrorism investigations from innocuous ones. We conducted an analysis of textual, spatial, temporal and social network features on data and metadata gathered from suspended Twitter content, and compared them with non-suspended content. Our analysis reveals a number of distinct characteristics of terrorism-related Twitter accounts. This work is a first step towards automated tools for the early detection of terrorism-related and extremist content on Twitter.

A multimodal analytics platform for journalists analysing large-scale, heterogeneous multilingual and multimedia content

Analysts and journalists face the problem of having to deal with very large, heterogeneous, and multilingual data volumes that need to be analyzed, understood, and aggregated. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing satisfaction from the user side.

Flood Relevance Estimation from Visual and Textual Content in Social Media Streams.

Disaster monitoring based on social media posts has raised a lot of interest in the domain of computer science the last decade, mainly due to the wide area of applications in public safety and security and due to the pervasiveness not solely on daily communication but also in life-threating situations. Social media can be used as a valuable source for producing early warnings of eminent disasters. This paper presents a framework to analyse social media multimodal content, in order to decide if the content is relevant to flooding. This is very important since it enhances the crisis situational awareness and supports various crisis management procedures such as preparedness. Evaluation on a benchmark dataset shows very good performance in both text and image classification modules.

A Topic Detection and Visualisation System on Social Media Posts

Large amounts of social media posts are produced on a daily basis and monitoring all of them is a challenging task. In this direction we demonstrate a topic detection and visualisation tool in Twitter data, which filters Twitter posts by topic or keyword, in two different languages; German and Turkish. The system is based on state-of-the-art news clustering methods and the tool has been created to handle streams of recent news information in a fast and user-friendly way. The user interface and user-system interaction examples are presented in detail.

A Hybrid Recommendation System Based on Density-Based Clustering

Collaborative filtering recommenders leverage past user-item ratings in order to predict ratings for new items. One of the most critical steps in such methods corresponds to the formation of the neighbourhood that contains the most similar users or items, so that the ratings associated with them can be employed for predicting new ratings. This work proposes to perform the combination of content-based and ratings-based evidence during the neighbourhood formation step and thus identify the most similar neighbours in a hybrid manner. To this end, DBSCAN, a density-based clustering approach, is applied for identifying the most similar users or items by considering the ratings-based and the content-based similarities, both individually and in combination. The resulting hybrid cluster-based CF recommendation scheme is then evaluated on the latest small MovieLens100k dataset and the experimental results indicate the potential of the proposed approach.