Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships

Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated\ud by Collaborative Filtering Systems (CFSs). Traditional CFSs\ud based on Matrix Factorization operate on the ratings provided\ud by users and have been recently extended to incorporate\ud demographic aspects such as age and gender. In this paper we\ud propose to merge CF techniques based on Matrix Factorization\ud and information regarding social friendships in order to\ud provide users with more accurate suggestions and rankings\ud on items of their interest. The proposed approach has been\ud evaluated on a real-life online social network; the experimental\ud results show an improvement against existing CF approaches.\ud A detailed comparison with related literature is also presen

Enhancing community detection using a network weighting strategy

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph tranversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.Comment: 28 pages, 2 figure

On Facebook, most ties are weak

Pervasive socio-technical networks bring new conceptual and technological challenges to developers and users alike. A central research theme is evaluation of the intensity of relations linking users and how they facilitate communication and the spread of information. These aspects of human relationships have been studied extensively in the social sciences under the framework of the "strength of weak ties" theory proposed by Mark Granovetter.13 Some research has considered whether that theory can be extended to online social networks like Facebook, suggesting interaction data can be used to predict the strength of ties. The approaches being used require handling user-generated data that is often not publicly available due to privacy concerns. Here, we propose an alternative definition of weak and strong ties that requires knowledge of only the topology of the social network (such as who is a friend of whom on Facebook), relying on the fact that online social networks, or OSNs, tend to fragment into communities. We thus suggest classifying as weak ties those edges linking individuals belonging to different communities and strong ties as those connecting users in the same community. We tested this definition on a large network representing part of the Facebook social graph and studied how weak and strong ties affect the information-diffusion process. Our findings suggest individuals in OSNs self-organize to create well-connected communities, while weak ties yield cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/ for the final versio

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

Crawling Facebook for Social Network Analysis Purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u

On the stability of metal nanoparticles synthesized by laser ablation in liquids

Nanoparticles (NPs) synthesized through chemical routes are stabilized by a surface layer of capping agents. These molecules, beside avoid the infinite growth of the solid phase, impart steric or electrostatic repulsive inter- particle interactions. The technique known as “Laser ablation in liquid” (LAL) is an alternative technique to synthesize capping agents-free metal nanoparticles.1 LAL involves focused laser pulsed irradiation of a bulk metal target in a liquid and consist of four stages . Laser-matter interaction, plasma induction, cavitation bubble formation and particle release in solution. Strikingly, LAL leads to the formation of very stable “naked” NPs that are long standing for months. It is worth emphasizing that the stabilization of noble metal colloids in water is challenging because of the large Hamaker constant. Noble metal NPs prepared by LAL have a large negative zeta-potential and therefore their stability should be electrostatic in nature and it is due to the presence negative surface charges. The question is what is the origin of these surface charges? Common explanations for this phenomenon involve the presence of gold oxides and/or the anion adsorption.2, 3 However, the presence of oxidized gold species on the surface of NPs prepared in water has been recently questioned on the basis of XPS analysis.4 Very recently we have accumulated evidences that, in the case of gold NPs prepared by LAL, the metal oxidation and anion adsorptions have only a minor role on building the negative surface potential and we proposed that excess electrons formed within the plasma phase could charge the gold particles.5 The figure below describes an experiment that points in this direction: the addition of macroscopic metallic objects induce the loss of charge (as seen in the temporal evolution of the zeta-potential) and eventually NPs aggregation pnly the case of gold NP synthesized by LAL while it is ineffective in the case of NP synthesized by the classical Turkevitch chemical reduction of HAuCl4 reduction (see the picture of the cuvettes after 4 days). Please click Additional Files below to see the full abstract

Application of an immunoproteomic approach to detect anti-profilin antibodies in sera of paritaria judaica allergic patients

Pollen from grasses, weeds, and trees constitutes one of the main sources of inhalant allergens frequently associated with seasonal patterns of allergic diseases. Pollen allergens show some analogies in the amino acids sequence which determine immunological similarity and cross reactivity. Parietaria judaica (P.j) pollen represents one of the main sources of allergens in the Mediterranean area and its major allergens have already been identified (Par j 1 and Par j 2). Recently, has been also described a minor allergen, profilin (Par j 3), an allergen present in pollen of trees, grasses and weeds. Allergenic plant profilins constitute a highly conserved family with sequence identities of 70% to 85% responsible for a wide range of cross-reactivity among pollens and plant foods. In this work we use an immunoproteomic approach to detect IgE antibodies against profilin in serum of P.j allergic patients

Temporal Logic Monitoring Rewards via Transducers

In Markov Decision Processes (MDPs), rewards are assigned according to a function of the last state and action. This is often limiting, when the considered domain is not naturally Markovian, but becomes so after careful engineering of extended state space. The extended states record information from the past that is sufficient to assign rewards by looking just at the last state and action. Non-Markovian Reward Decision Processes (NRMDPs) extend MDPs by allowing for non-Markovian rewards, which depend on the history of states and actions. Non-Markovian rewards can be specified in temporal logics on finite traces such as LTLf/LDLf, with the great advantage of a higher abstraction and succinctness; they can then be automatically compiled into an MDP with an extended state space. We contribute to the techniques to handle temporal rewards and to the solutions to engineer them. We first present an approach to compiling temporal rewards which merges the formula automata into a single transducer, sometimes saving up to an exponential number of states. We then define monitoring rewards, which add a further level of abstraction to temporal rewards by adopting the four-valued conditions of runtime monitoring; we argue that our compilation technique allows for an efficient handling of monitoring rewards. Finally, we discuss application to reinforcement learning