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Publications

Publications by Rui Portocarrero Sarmento

2016

Dynamic community detection in evolving networks using locality modularity optimization

Authors
Cordeiro, M; Sarmento, RP; Gama, J;

Publication
SOCIAL NETWORK ANALYSIS AND MINING

Abstract
The amount and the variety of data generated by today's online social and telecommunication network services are changing the way researchers analyze social networks. Facing fast evolving networks with millions of nodes and edges are, among other factors, its main challenge. Community detection algorithms in these conditions have also to be updated or improved. Previous state-of-the-art algorithms based on the modularity optimization (i.e. Louvain algorithm), provide fast, efficient and robust community detection on large static networks. Nonetheless, due to the high computing complexity of these algorithms, the use of batch techniques in dynamic networks requires to perform network community detection for the whole network in each one of the evolution steps. This fact reveals to be computationally expensive and unstable in terms of tracking of communities. Our contribution is a novel technique that maintains the community structure always up-to-date following the addition or removal of nodes and edges. The proposed algorithm performs a local modularity optimization that maximizes the modularity gain function only for those communities where the editing of nodes and edges was performed, keeping the rest of the network unchanged. The effectiveness of our algorithm is demonstrated with the comparison to other state-of-the-art community detection algorithms with respect to Newman's Modularity, Modularity with Split Penalty, Modularity Density, number of detected communities and running time.

2015

Retrieval, visualization and validation of affinities between documents

Authors
Trigo, L; Víta, M; Sarmento, R; Brazdil, P;

Publication
IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract
We present an Information Retrieval tool that facilitates the task of the user when searching for a particular information that is of interest to him. Our system processes a given set of documents to produce a graph, where nodes represent documents and links the similarities. The aim is to offer the user a tool to navigate in this space in an easy way. It is possible to collapse/expand nodes. Our case study shows affinity groups based on the similarities of text production of researchers. This goes beyond the already established communities revealed by co-authorship. The system characterizes the activity of each author by a set of automatically generated keywords and by membership to a particular affinity group. The importance of each author is highlighted visually by the size of the node corresponding to the number of publications and different measures of centrality. Regarding the validation of the method, we analyse the impact of using different combinations of titles, abstracts and keywords on capturing the similarity between researchers.

2016

Social Network Analysis in Streaming Call Graphs

Authors
Sarmento, R; Oliveira, M; Cordeiro, M; Tabassum, S; Gama, J;

Publication
Studies in Big Data

Abstract
Mobile phones are powerful tools to connect people. The streams of Call Detail Records (CDR’s) generating from these devices provide a powerful abstraction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale and generated in real-time, the underlying social networks are inherently complex and, thus, difficult to analyze. Conventional data analysis performed by telecom operators is slow, done by request and implies heavy costs in data warehouses. In face of these challenges, real-time streaming analysis becomes an ever increasing need to mobile operators, since it enables them to quickly detect important network events and optimize business operations. Sampling, together with visualization techniques, are required for online exploratory data analysis and event detection in such networks. In this chapter, we report the burgeoning body of research in network sampling, visualization of streaming social networks, stream analysis and the solutions proposed so far. © 2016, Springer International Publishing Switzerland.

2017

Comparative approaches to using R and Python for statistical data analysis

Authors
Sarmento, R; Costa, V;

Publication
Comparative Approaches to Using R and Python for Statistical Data Analysis

Abstract
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

2017

Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Complex Networks & Their Applications VI - Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), COMPLEX NETWORKS 2017, Lyon, France, November 29 - December 1, 2017.

Abstract
Social Network Analysis (SNA) is an important research area. It originated in sociology but has spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. This has stimulated research on how to support SNA with the development of new algorithms. One of the critical areas involves calculation of different centrality measures. The challenge is how to do this fast, as many increasingly larger datasets are available. Our contribution is an incremental version of the Laplacian Centrality measure that can be applied not only to large graphs but also to dynamically changing networks. We have conducted several tests with different types of evolving networks. We show that our incremental version can process a given large network, faster than the corresponding batch version in both incremental and full dynamic network setups. © Springer International Publishing AG 2018.

2015

Visualization for streaming telecommunications networks

Authors
Sarmento, R; Cordeiro, M; Gama, J;

Publication
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Abstract
Regular services in telecommunications produce massive volumes of relational data. In this work the data produced in telecommunications is seen as a streaming network, where clients are the nodes and phone calls are the edges. Visualization techniques are required for exploratory data analysis and event detection. In social network visualization and analysis the goal is to get more information from the data taking into account actors at the individual level. Previous methods relied on aggregating communities, k-Core decompositions and matrix feature representations to visualize and analyse the massive network data. Our contribution is a group visualization and analysis technique of influential actors in the network by sampling the full network with a top-k representation of the network data stream. © Springer International Publishing 2015.

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