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.

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.

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.