Abstract
The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.

Abstract
Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.

Abstract
An important dimension of complex networks is embedded in the weights of its edges. Incorporating this source of information on the analysis of a network can greatly enhance our understanding of it. This is the case for gene co-expression networks, which encapsulate information about the strength of correlation between gene expression profiles. Classical un-weighted gene co-expression networks use thresholding for defining connectivity, losing some of the information contained in the different connection strengths. In this paper, we propose a mining method capable of extracting information from weighted gene co-expression networks. We study groups of differently connected nodes and their importance as network motifs. We define a subgraph as a motif if the weights of edges inside the subgraph hold a significantly different distribution than what would be found in a random distribution. We use the Kolmogorov-Smirnov test to calculate the significance score of the subgraph, avoiding the time consuming generation of random networks to determine statistic significance. We apply our approach to gene co-expression networks related to three different types of cancer and also to two healthy datasets. The structure of the networks is compared using weighted motif profiles, and our results show that we are able to clearly distinguish the networks and separate them by type. We also compare the biological relevance of our weighted approach to a more classical binary motif profile, where edges are unweighted. We use shared Gene Ontology annotations on biological processes, cellular components and molecular functions. The results of gene enrichment analysis show that weighted motifs are biologically more significant than the binary motifs.

Abstract
Nodes in complex networks inherently represent different kinds of functional or organizational roles. In the dynamic process of an information cascade, users play different roles in spreading the information: some act as seeds to initiate the process, some limit the propagation and others are in-between. Understanding the roles of users is crucial in modeling the cascades. Previous research mainly focuses on modeling users behavior based upon the dynamic exchange of information with neighbors. We argue however that the structural patterns in the neighborhood of nodes may already contain enough information to infer users' roles, independently from the information flow in itself. To approach this possibility, we examine how network characteristics of users affect their actions in the cascade. We also advocate that temporal information is very important. With this in mind, we propose an unsupervised methodology based on ensemble clustering to classify users into their social roles in a network, using not only their current topological positions, but also considering their history over time. Our experiments on two social networks, Flickr and Digg, show that topological metrics indeed possess discriminatory power and that different structural patterns correspond to different parts in the process. We observe that user commitment in the neighborhood affects considerably the influence score of users. In addition, we discover that the cohesion of neighborhood is important in the blocking behavior of users. With this we can construct topological fingerprints that can help us in identifying social roles, based solely on structural social ties, and independently from nodes activity and how information flows.

Abstract

Abstract
It is well known that many social networks follow the homophily principle, dictating that individuals tend to connect with similar peers. Past studies focused on non-topological properties, such as the age, gender, beliefs or educations. In this paper we focus precisely on the topology itself, exploring the possible existence of pairwise role dependency, that is, purely structural homophily. We show that while pairwise dependency is necessary for some structural roles, it may be misleading for others. We also present SR-Diffuse, a novel method for identifying the structural roles of nodes within a network. It is an iterative algorithm following an optimization model able to learn simultaneously from topological features and structural homophily, combining both aspects. For assessing our method, we applied it in a classification problem in information cascades, comparing its performance against several baseline methods. The experimental results with Flickr and Digg data show that SR-Diffuse can improve the quality of the discovered roles and can better represent the profile of the individuals, leading to a better prediction of social classes within information cascades.