In this article, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

According to the current design of content sharing services, such as Online Social Networks (OSNs), typically (i) the service provider has unrestricted access to the uploaded resources and (ii) only the user uploading the resource is allowed to define access control permissions over it. This results in a lack of control from other users that are associated, in some way, with that resource. To cope with these issues, in this paper, we propose a privacy-preserving system that allows users to upload their resources encrypted, and we design a collaborative multi-party access control model allowing all the users related to a resource to participate in the specification of the access control policy. Our model employs a threshold-based secret sharing scheme, and by exploiting users' social relationships, sets the trusted friends of the associated users responsible to partially enforce the collective policy. Through replication of the secret shares and delegation of the access control enforcement role, our model ensures that resources are timely available when requested. Finally, our experiments demonstrate that the performance overhead of our model is minimal and that it does not significantly affect user experience.

Today, a considerable proportion of the public political discourse that proceeds nationwide elections is happening through Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis and sarcasm correction. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.

We propose a flow propagation algorithm (FlowPro) that finds the community surrounding a node in a complex network. In each iteration of the main process of FlowPro, the initial node propagates a flow that is shared among its neighbors. Each node is able to store, propagate to its neighbors, and return, part of the flow it receives to the initial node. When the algorithm converges, the flow stored in the nodes that belong to the community of the initial node, is generally higher than the flow stored in the rest of the graph nodes, thus the requested community emerges. The novelty of the proposed approach lies in the fact that FlowPro is local, allows to visualize the community and does not require the knowledge of the entire graph as most of the existing methods found in the literature. This makes possible the application of FlowPro in extremely large graphs or in cases where the entire graph is unknown like in most social networks.

Networks in various application domains present an internal structure, where nodes form groups of tightly connected components which are more loosely connected to the rest of the network. Several attempts have been made to provide a formal definition to the generally described "community finding" concept, providing different approaches. Some algorithms follow an iterative approach starting by characterizing either the entire network, or each individual node as community, and splitting [1] or merging communities respectively, producing a hierarchical tree of nested communities, called dendrogram. Several researchers aim to find the entire hierarchical community dendrogram [1] while others wish to identify only the optimal community partition. Some researchers aim at discovering distinct (non-overlapping) communities, while others allow for overlaps [2]. The Blondel algorithm described by Blondel et al. in [3], follows a bottom-up approach. Each node in the graph comprises a singleton community. Two communities are merged into one if the resulting community has larger modularity value that both the initial ones. This is a fast and accurate algorithm which, detects all communities in the graph. In suffers however in the sense that it constantly, during its execution, requires the knowledge of a global information of the graph, namely the number of its edges (which change as the algorithm modifies the graph), limiting its distributed nature. The Infomap algorithm [4] transforms the problem of community detection into efficiently compressing the structure of the graph, so that one can recovered almost the entire structure from the compressed form. This is achieved by minimizing a function that expresses the tradeoff between compression factor and loss of information (difference between the original graph and the reconstructed graph).

One of the most appealing characteristics of unstructured P2P overlays is their enhanced self-* properties, which results from their loose, random structure. In addition, most of the algorithms which make searching in unstructured P2P systems scalable, such as dynamic querying and 1-hop replication, rely on the random nature of the overlay to function efficiently. The underlying communications network (i.e., the Internet), however, is not as randomly constructed. This leads to a mismatch between the distance of two peers on the overlay and the hosts they reside on at the IP layer, which in turn leads to its misuse. The crux of the problem arises from the fact that any effort to provide a better match between the overlay and the IP layer will inevitably lead to a reduction in the random structure of the P2P overlay, with many adverse results. With this in mind, we propose ITA, an algorithm which creates a random overlay of randomly connected neighborhoods providing topology awareness to P2P systems, while at the same time has no negative effect on the self-* properties or the operation of the other P2P algorithms. Using extensive simulations, both at the IP router level and autonomous system level, we show that ITA reduces communication latencies by as much as 50 percent. Furthermore, it not only reduces by 20 percent the number of IP network messages which is critical for ISPs carrying the burden of transporting P2P traffic, but also distributes the traffic load more evenly on the routers of the IP network layer.

The paper performs an empirical study of the My Space Online Social Network (OSN). It aims to capture the evolution of user population, to examine user activity, and finally to characterize community formation using two well established community finding algorithms, namely the Fortuna to et al. and the Clique Percolation algorithms. Both algorithms are known to be effective in identifying communities in large graphs, starting at seed nodes and utilizing only local interactions between nodes. One million user profiles were randomly collected in a month's period. For each profile certain attributes were fetched: profile status (public, private, invalid), member since and last login dates, number of friends, number of views, etc. The profiles and their attributes were analyzed in order to reveal the evolution in user population and the activity of the participating members. Significant conclusions were drawn for the synthesis of the population based on profile status, the number of friends, and the duration My Space members stay active. Subsequently, a large number of communities were identified aiming to reveal the structure of the underlying social network graph. The collected data were further analyzed in order to characterize community size and density but also to retrieve correlations in the activity among members of the same community. A total of 171 communities were detected with Fortunato's algorithm, while using Clique Percolation this number was 201. Results demonstrate that My Space members tend to form dense communities. For the first time, strong correlation in the last login date (the main attribute that shows user activity) for members of the same community was documented. It was also shown that members participating in the same community have similar values for other attributes like for example number of friends. Lastly, there is strong evidence that participation of users in communities inhibits them from abandoning My Space.

In this paper, we propose an algorithm that finds the entire community structure of a network, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters.

Purpose The purpose of this paper is to present the operational framework of SafeLine ( www.safeline.gr ), the Greek Internet Hotline. SafeLine's mission is to eliminate illegal internet content with emphasis on images of child sexual abuse and to combat against users who encroach on children's rights through illegal internet activities like grooming or cyberbullying. Design/methodology/approach SafeLine serves as a contact point for those individuals that wish to report illegal internet content or activities that they encounter while surfing the internet. Upon receipt of a report verified as illegal, SafeLine forwards it to the Greek police, to other governmental authorities, or to the hotline of another country. Findings An analysis of the reports received by SafeLine indicate a clear shift on the type of reported content. A large number of recent reports concern financial fraud and personal data violation, activity related to the newly emerged social networking sites. Practical implications The resulting aim of this paper is to inform internet users about their ability to report illegal internet material. Social implications Interesting topics such as internet addiction, illegal activities in the emerging social networking systems and legislation regarding cyber crimes will also be analyzed while trying to give an overall view about internet risks for children. Finally, useful tips for the public are presented as a means of promoting a safer internet for children and adults. Originality/value The paper reports on the latest trends in illegal internet content based upon an analysis of the reports received by SafeLine. A clear shift to illegal activity in social networking sites clearly emerges.

The random nature of unstructured P2P overlays imbues them with enhanced self-*properties. Most of the algorithms which make searching in unstructured P2P systems scalable, such as dynamic querying and 1-hop replication, rely on the random nature of the overlay to function efficiently. However, they do not take into account the structure of the underlying physical communications network, which is anything but random. Efforts to provide topology awareness to unstructured P2P systems often result to clustered graphs which affect negatively algorithms that rely on random overlays. In this paper, we propose ITA, an algorithm which creates a random overlay of randomly connected neighborhoods providing topology awareness to P2P systems, while at the same time has no negative effect on the self-*properties or the operation of the other P2P algorithms. Using extensive simulations, we demonstrate that ITA reduces communication latency by as much as 50% which is important for P2P users. Furthermore, it reduces by 20% the number of IP network messages which is critical for ISPs carrying the burden of transporting P2P traffic. Finally, ITA is shown to reduce significantly the load imposed on the routers of the IP network layer.

Given an undirected graph with nonnegative weights on its edges, a group of source nodes, and a group of destination nodes, we investigate the problem of constructing a multicast tree that minimizes the sum of distances from a destination node to all sources. This problem has been proven to be NP-complete. In this article, we show that there is a point c of the graph such that a shortest paths tree constructed from c is a 2-approximation to the problem, a significant improvement over the previous results, and we present an efficient approximation algorithm. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 47(3), 178–183 2006

Data communication and fault tolerance are important issues in parallel computers in which the processors are interconnected according to a specific topology. One way to achieve fault tolerant interprocessor communication is by exploiting the disjoint paths that exist between pairs of source and destination nodes. We construct n-1 directed edge disjoint spanning trees on the star network. These spanning trees are used to derive a near optimal single node broadcasting algorithm, and fault tolerant algorithms for the single node and multinode broadcasting, and for the single node and multinode scattering problems. Broadcasting is the distribution of the same group of messages from one processor to all the other processors. Scattering is the distribution of distinct groups of messages from one processor to all the other processors. We consider broadcasting and scattering from a single processor of the network and simultaneously from all processors of the network. The single node broadcasting algorithm offers a speed up of n-1 for a large number of messages, over the straightforward algorithm that uses a single shortest path spanning tree. Fault tolerance is achieved by transmitting the same messages through a number of edge disjoint spanning trees. The fault tolerant algorithms operate successfully in the presence of up to n-2 faulty nodes or edges in the network. No prior knowledge of the faulty nodes or edges is required. All of the algorithms operate under the store and forward, all port communication model.

The n-star graph, denoted by S/sub n/, is one of the graph networks that have been recently proposed as attractive alternatives to the n-cube topology for interconnecting processors in parallel computers. We present a parallel algorithm for the computation of the Fourier transform on the star graph. The algorithm requires O(n/sup 2/) multiply-add steps for an input sequence of n! elements, and is hence cost-optimal with respect to the sequential algorithm on which it is based. This is believed to be the first algorithm, and the only one to date, for the computation of the Fourier transform on the star graph.>