Abstract
Social networks as Facebook and Twitter gained a remarkable attention in the last decade. A huge amount of data is emerging and posted everyday by users that are becoming more interested in and relying on social network for information, news and opinions. Real time posting came to rise and turned easier to report news and events. However, due to its dimensions, in this work we focus on building a system architecture capable of detecting journalistic relevance of posts automatically on this 'haystack' full of data. More specifically, users will have the change to interact with a 'friendly user interface' which will provide several tools to analyze data. © 2017.

Abstract
The structural patterns in the neighborhood of nodes assign unique roles to the nodes. Mining the set of existing roles in a network provides a descriptive profile of the network and draws its general picture. This paper proposes a new method to determine structural roles in a dynamic network based on the current position of nodes and their historic behavior. We develop a temporal ensemble clustering technique to dynamically find groups of nodes, holding similar tempo-structural roles. We compare two weighting functions, based on age and distribution of data, to incorporate temporal behavior of nodes in the role discovery. To evaluate the performance of the proposed method, we assess the results from two points of view: 1) goodness of fit to current structure of the network; 2) consistency with historic data. We conduct the evaluation using different ensemble clustering techniques. The results on real world networks demonstrate that our method can detect tempo-structural roles that simultaneously depict the topology of a network and reflect its dynamics with high accuracy. Copyright 2017 ACM.

Abstract
We address the problem of whether networks of mobile devices such as smart-phones or tablets can be used to perform opportunistic, best-effort, parallel computations. We designed and implemented P3-Mobile, a parallel programming system for edge-clouds of Android devices to test the feasibility of this idea. P3-Mobile comes with a programming model that supports parallel computations over peer-to-peer overlays mapped onto mobile networks. The system performs automatic load-balancing by using the overlay to discover work. We present preliminary performance results for a parallel benchmark, using up to 16 devices, and discuss their implications towards future work. Copyright © 2017 ACM.

Abstract
With recent advances in high-throughput cell biology, the amount of cellular biological data has grown drastically. Such data is often modeled as graphs (also called networks) and studying them can lead to new insights intomolecule-level organization. A possible way to understand their structure is by analyzing the smaller components that constitute them, namely network motifs and graphlets. Graphlets are particularly well suited to compare networks and to assess their level of similarity due to the rich topological information that they offer but are almost always used as small undirected graphs of up to five nodes, thus limiting their applicability in directed networks. However, a large set of interesting biological networks such asmetabolic, cell signaling, or transcriptional regulatory networks are intrinsically directional, and using metrics that ignore edge direction may gravely hinder information extraction. Our main purpose in this work is to extend the applicability of graphlets to directed networks by considering their edge direction, thus providing a powerful basis for the analysis of directed biological networks. We tested our approach on two network sets, one composed of synthetic graphs and another of real directed biological networks, and verified that they were more accurately grouped using directed graphlets than undirected graphlets. It is also evident that directed graphlets offer substantially more topological information than simple graph metrics such as degree distribution or reciprocity. However, enumerating graphlets in large networks is a computationally demanding task. Our implementation addresses this concern by using a state-of-the-art data structure, the g-trie, which is able to greatly reduce the necessary computation. We compared our tool to other state-of-the art methods and verified that it is the fastest general tool for graphlet counting.

Abstract
Crowd-sourcing the resources of mobile devices is a hot topic of research given the game-changing applications it may enable. In this paper we study the feasibility of using edge-clouds of mobile devices to reduce the load in traditional WiFi infrastructures for video dissemination applications. For this purpose, we designed and implemented a mobile application for video dissemination in sport venues that retrieves replays from a central server, through the access points in the WiFi infrastructure, into a smartphone. The fan's smartphones organize themselves into WiFi-Direct groups and exchange video replays whenever possible, bypassing the central server and access points. We performed a real-world experiment using the live TV feed for the Champions League game Benfica-Besiktas with the help of a group of volunteers using the application at the student's union lounge. The analysis of the logs strongly suggests that edge-clouds can significantly reduce the load in the access points at such large venues and improve quality of experience. Indeed, the edge-clouds formed were able to serve up to 80% of connected users and provide 56% of all downloads requested from within.

Abstract
In the last decade, technological advances and improved manufacturing processes have significantly dropped the price tag of mobile devices such as smartphones and tablets whilst augmenting their storage and computational capabilities. Their ubiquity fostered research on mobile edge-clouds, formed by sets of such devices in close proximity, with the goal of mastering their global computational and storage resources. The development of crowdsourcing applications that take advantage of such edge-clouds is, however, hampered by the complexity of network formation and maintenance, the intrinsic instability of wireless links and the heterogeneity of the hardware and operating systems in the devices. In this paper we present a middleware to deal with this complexity, providing a building block upon which crowd-sourcing applications may be built.We motivate the development of the middleware through a discussion of real-world applications, and present the middleware's architecture along with the associated components and current development status. The middleware takes form as a Java API for Android devices that allows for the establishment of links using heterogeneous communication technologies (e.g., Wifi-Direct, Bluetooth), and the combination of these links to form a logical edge-cloud network. On top of this functionality, services for edge computation, storage, and streaming are also being developed. © 2017 Association for Computing Machinery.