Overlapping Community Structure in Co-authorship Networks: a Case Study

Community structure is one of the key properties of real-world complex networks. It plays a crucial role in their behaviors and topology. While an important work has been done on the issue of community detection, very little attention has been devoted to the analysis of the community structure. In this paper, we present an extensive investigation of the overlapping community network deduced from a large-scale co-authorship network. The nodes of the overlapping community network represent the functional communities of the co-authorship network, and the links account for the fact that communities share some nodes in the co-authorship network. The comparative evaluation of the topological properties of these two networks shows that they share similar topological properties. These results are very interesting. Indeed, the network of communities seems to be a good representative of the original co-authorship network. With its smaller size, it may be more practical in order to realize various analyses that cannot be performed easily in large-scale real-world networks.Comment: 2014 7th International Conference on u- and e- Service, Science and Technolog

Assessment of Emissions from Cement Plants Using AERMOD Modeling

Coal combustion generates many gases and emissions which are harmful to public health and the environment. So, it is necessary to assess the health risks for the people living in the nearby cement plants that use coal as a fuel. In this article, a health risk assessment HRA was carried out concerning the air emissions from a cement plant in the heavy industry area located at Beni Suef governorate - Egypt. The article handles the assessment of the health risks for pollutants classified as non-carcinogenic i.e. sulfur dioxide - mercury and the health risks for pollutants classified as carcinogenic i.e. arsenic – chromium VI. An Air dispersion modeling program AERMOD is used to measure and evaluate long and short terms health impacts to expect the concentration of pollutants at the ground level within 30 km radius of the studied cement plant. The emissions measurements findings are used as input to the model in addition to some factors such as meteorology and surrounding terrain. Consequently, the program can implement simulations for the emissions concentration level of the mentioned pollutants and their effects on the population at Jazirat Abu Salih village, which is 10 km far from the studied cement plant. The results for mentioned pollutants concentrations levels matched with acceptance and safe levels of ambient air quality standards. In addition, the increment lifetime cancer risk ILCR by inhalation was calculated for arsenic and chromium and all results conformed with the safe and accepted limits

A New Path Generation Algorithm Based on Accurate NURBS Curves

International audienceThe process of finding an optimum, smooth and feasible global path for mobile robot navigation usually involves determining the shortest polyline path, which will be subsequently smoothed to satisfy the requirements. Within this context, this paper deals with a novel roadmap algorithm for generating an optimal path in terms of Non-Uniform Rational B-Splines (NURBS) curves. The generated path is well constrained within the curvature limit by exploiting the influence of the weight parameter of NURBS and/or the control points' locations. The novelty of this paper lies in the fact that NURBS curves are not used only as a means of smoothing, but they are also involved in meeting the system's constraints via a suitable parameterization of the weights and locations of control points. The accurate parameterization of weights allows for a greater benefit to be derived from the influence and geometrical effect of this factor, which has not been well investigated in previous works. The effectiveness of the proposed algorithm is demonstrated through extensive MATLAB computer simulations

Generic discounting evaluation approach for urban image classification

International audienceBelief function theory provides a robust framework for uncertain information modeling. It also offers several fusion tools in order to profit from multi-source context. Nevertheless, fusion is a sensible task where conflictual information may appear especially when sources are unreliable. Therefore, measuring source's reliability has been the center of many research and development. Existing solutions for source's reliability estimation are based on the assumption that distance is the only factor for conflictual situations. Indeed, integrating only distance measures to estimate source's reliability is not sufficient where source's confusion may be also considered as conflict origin. In this paper, we tackle reliability estimation and we introduce a new discounting operator that considers those two possible conflict origins. We propose an automatic method for discounting factor calculation. Those factors are integrated on belief classifier and tested on high-resolution image classification problem

Overlapping Community Detection Versus Ground-Truth in AMAZON Co-Purchasing Network

International audienceObjective evaluation of community detection algorithms is a strategic issue. Indeed, we need to verify that the communities identified are actually the good ones. Moreover, it is necessary to compare results between two distinct algorithms to determine which is most effective. Classically, validations rely on clustering comparison measures or on quality metrics. Although, various traditional performance measures are used extensively. It appears very clearly that they cannot distinguish community structures with different topological properties. It is therefore necessary to propose an alternative methodology more sensitive to the community structure variations in order to conduct more effective comparisons. In this paper, we present a framework to tackle this challenge through a comprehensive analysis of the community structure of overlapping community structured networks. We illustrate our approach with an experimental analysis of a real-world network with a ground-truth community structure that we compare with the output of eight different overlapping community detection procedures, representative of categories of popular algorithms available in the literature. The results allow a better understanding of their behavior. Furthermore, they demonstrate that more emphasis should be put on the topology of the uncovered community structure in order to evaluate the effectiveness of community detection algorithms

User and group networks on YouTube: A comparative analysis

International audienceYouTube is the largest video-sharing social network where users (aka channels) can create links to any other users. Moreover, initially, users were allowed to create and join special groups of interest. Therefore, two types of online social networks can be defined. First, a user network where the nodes represent the users and the edges represent the social ties (friendship) between users. Second, a group network where the nodes represent the groups and the edges represent the social ties between groups, due to shared users. As the group network can be apprehended as the ground-truth overlapping community graph (where the nodes are the discovered communities and the links represent the overlap between the communities) of the user network, it is of prime interest to analyze and compare their topological structure. In this paper, we report the results of an extensive comparative evaluation of various macroscopic topological properties of both networks based on data from over one million users. Additionally, the community structure of the networks are uncovered using an overlapping community detection algorithm and the relationship between their community structure is investigated. The results of this study allow a better understanding of the relations between the mesoscopic and the macroscopic properties of online social networks, both from a topological and a functional point of view

Kohonen's Map Approach for the Belief Mass Modeling

International audienceIn the framework of the evidence theory, several approaches for estimating belief functions are proposed. However, they generally suffer from the problem of masses attribution in case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using Kohonen's map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision and paradox. The proposed method for basic belief assignment (BBA) is of interest for solving estimation mass functions problems where a large quantity of multi-variate data is available. Indeed, the use of Kohonen map simplifies the process of assigning mass functions. The proposed method has been compared to state-of-the art BBA technique on benchmark database and applied on remote sensing data for image classifi- cation purpose. Experimentation shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle large amount of data