ICCS 2017 Main Track (MT) Session 1
Time and Date: 10:35 - 12:15 on 12th June 2017
Room: HG F 30
Chair: Youssef Nashed
|525|| Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory [abstract]
Abstract: This paper presents results of topic modeling and networks of topics using the ICCS corpus, which contains domain specific(computational science) papers over sixteen years (5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to topicality of various problems, technologies and methods, and how these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks of the topic modeling results and the authors’ keywords. The results of this study will help ICCS organizers to identify the past and future trends of core talking topics, and to organize workshops based on communities of topics which in return will satisfy the interests of participants by allowing them to attend the workshop which is directly related to their domain area. We used Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped the results hierarchically. We used Gephi to study static networks of topics, and R library called DyA to analyze dynamic networks of topics.
|Tesfamariam Abuhay, Sergey Kovalchuk, Klavdiya Bochenina, George Kampis, Valeria Krzhizhanovskaya and Michael Lees|
|43|| Identifying Urban Inconsistencies via Street Networks [abstract]
Abstract: Street networks, comprised by its topology and geometry, can be used in problems related to ill-designed urban structures. Several works have focused on such application. Nevertheless, they lack a clear methodology to characterize and explain the urban space through a complex network. Aided by topo-geometrical measures from georeferenced networks, we present a methodology to identify what we call urban inconsistencies, which are characterized by low-access regions containing nodes that lack efficient access from or to other regions in a city. To this end, we devised algorithms capable of preprocessing and analyzing street networks, pointing to existing mobility problems in a city. Mainly, we identify inconsistencies that pertain to a given node where a facility of interest is currently placed. Our results introduce ways to assist in the urban planning and design processes. The proposed techniques are discussed through the visualization and analysis of a real-world city. Hence, our contributions provide a basis for further advancements on street networks applied to facilities location analysis.
|Gabriel Spadon, Gabriel Gimenes and Jose Rodrigues-Jr|
|120|| Impact of Neighbors on the Privacy of Individuals in Online Social Networks [abstract]
Abstract: The problem of user privacy enforcement in online social networks (OSN) cannot be ignored and, in recent years, Facebook and other providers have improved considerably their privacy protection tools. However, in OSN's the most powerful data protection "weapons" are the users themselves. The behavior of an individual acting in an OSN highly depends on her level of privacy attitude: an aware user tends not to share her private information, or the private information of her friends, while an unaware user could not recognize some information as private, and could share it without care to her contacts. In this paper, we experimentally study the role of the attitude on privacy of an individual and her friends on information propagation in social networks. We model information diffusion by means of an extension of the Susceptible-Infectious-Recovered (SIR) epidemic model that takes into account the privacy attitude of users. We employ this diffusion model in stochastic simulations on a synthetic social network, designed for miming the characteristics of the Facebook social graph.
|Livio Bioglio and Ruggero G. Pensa|
|230|| Mining Host Behavior Patterns From Massive Network and Security Logs [abstract]
Abstract: Mining host behavior patterns from massive logs plays an important and crucial role in anomalies diagnosing and management for large-scale networks. Almost all prior work gives a macroscopic link analysis of network events, but fails to microscopically analyze the evolution of behavior patterns for each host in networks. In this paper, we propose a novel approach, namely Log Mining for Behavior Pattern (LogM4BP), to address the limitations of prior work. LogM4BP builds a statistical model that captures each host's network behavior patterns with the nonnegative matrix factorization algorithm, and finally improve the interpretation and comparability of behavior patterns, and reduce the complexity of analysis. The work is evaluated on a public data set captured from a big marketing company. Experimental results show that it can describe network behavior patterns clearly and accurately, and the significant evolution of behavior patterns can be mapped to anomaly events in real world intuitively.
|Jing Ya, Tingwen Liu, Quangang Li, Jinqiao Shi, Haoliang Zhang, Pin Lv and Li Guo|