Contemporary techniques for identifying key nodes in complex networks typically rely on the static topology of the network, often neglecting the potential dynamic information available. We introduce a novel centrality measurement approach named gravity box-coverage and effective distance (GBED). It capitalizes on the notion that the internal structure of the gravity box encapsulates crucial information about nodes. It transforms static Euclidean distance into dynamic effective distance (ED), extracting concealed insights through an analysis of both static and dynamic topological paths. Initially, the ED between nodes is computed based on node arrival probabilities. Subsequently, the box-coverage algorithm defines the influence area of nodes. The improved gravity model is then applied to estimate the interaction ability between nodes. Finally, the local influence capability score of the node’s box, covering the influence region, is calculated. The global influence capability score of the node is aggregated according to the neighborhood rule. We compare it with five established methods based on nine real-world networks. In the SIR epidemic spreading, the nodes identified by GBED exhibit a broader range of influence, and the correlation between estimated influences of nodes from GBED and real influences by simulation is higher than correlations associated with other algorithms.

Graph contrastive learning has demonstrated outstanding performance in addressing the issue of label scarcity. It still has two limitations: 1) the stacking of graph layers often leads to over-smoothing, hard to distinguish the embeddings of distinct nodes; and 2) traditional algorithms typically model user preferences with a unified intention, neglecting the multifaceted and fine-grained motivations behind user-item interactions. To overcome these shortcomings, we propose an adaptive disentangled contrastive learning (AdaDCL) method tailored for recommendation systems. First, we perform disentangled modeling of global information intentions and introduce a cross-view contrastive learning task, employing a parameterized mask generator for adaptive augmentation. Second, we employ a layer attention mechanism to counter over-smoothing in GNNs, ensuring that meaningful semantic features are preserved across layers. Third, an adaptive hardness negative sampling (AHNS) strategy dynamically selects negative samples based on their hardness levels, reducing the risk of false positives and negatives while enhancing the robustness of contrastive learning. Comprehensive experiments on three benchmark datasets, including Gowalla, and comparisons against twelve state-of-the-art baseline models (e.g., DisenHAN), demonstrate that AdaDCL surpasses the classic LightGCN by 5.89% in Recall@20 and over 7% in NDCG@20 on the Gowalla dataset. These results highlight the effectiveness and generalizability of our approach.

The proliferation of Massive Open Online Courses (MOOCs) has created an urgent need for advanced course recommendation systems (RS). Course recommendations in MOOCs require transparent motivations to justify course selection, as there are often many courses with the same title, but which vary widely in content, duration, learning resources provided, and the academic authority of the instructor. Explainable recommendations are crucial to ensure that recommended courses fit well with learners’ needs and increase the chance of successful course completion, but unfortunately existing RS for MOOCs struggle to provide explainable recommendations. In this article, we present KnowPath , a novel RS for MOOCs, which generates effective and explainable recommendations. KnowPath uses open source Large Language Models (LLMs) to construct knowledge graphs (KGs) capable of accurately capturing complex relationships between MOOC entities (e.g., learners, instructors, educational resources) and employs Reinforcement Learning to align the output of an LLM with learner preferences. Extensive experiments on two public datasets (XueTang and COCO) demonstrate the superior performance and generalizability of KnowPath , underlining its potential to revolutionize the field of personalized online education.

The Dynamic Retrieval Augmented Generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). However, current dynamic RAG methods fall short in both aspects: identifying the optimal moment to activate the retrieval module and crafting the appropriate query once retrieval is triggered. To overcome these limitations, we introduce an approach, namely, RaDIO, Real-Time Hallucination Detection with Contextual Index Optimized query formulation for dynamic RAG. The approach is specifically designed to make decisions on when and what to retrieve based on the LLM’s real-time information needs during the text generation process. We evaluate RaDIO along with existing methods comprehensively over several knowledge-intensive generation datasets. Experimental results show that RaDIO achieves superior performance on all tasks, demonstrating the effectiveness of our work.

Knowledge graphs (KGs) have gained popularity in many areas, such as question-answering and recommendation systems, because of their robust knowledge representation capabilities. Real-world KGs are generally incomplete, and therefore significant research efforts have been devoted to finding effective ways to extend the knowledge encapsulated in a KG. Recently, large-language models (LLMs) have been used to enrich the representation of entities and relations in a KG. Such a strategy has its limitations, mainly due to the significant computational resources required by LLMs and the need for a custom query to fully exploit the power of the corresponding LLM. In this paper, we present a novel LLM-based approach to the KG completion task. Specifically, we model an KG triple as a text sequence, so that the entities and relations are used as prompts for the LLM. In this way, we can generate more accurate representations of the entities and relations of the KG. Our method is scalable, capable of running on modest hardware platforms, and replaces custom prompts with automatically generated ones. The effectiveness of our approach is demonstrated through experiments on three real-world datasets, where it achieves state-of-the-art performance in crucial tasks such as triple classification and relation prediction.

Recommender Systems in real scenarios suffer from poor representation ability of user–item interaction graph caused by data sparsity and data noise. Most of the existing models have problems of instability and limited generation ability. This article proposes a novel recommendation method called graph generative diffusion with hard negative sampling contrastive learning recommendation method (GGDHSCL) to overcome the limitations above. First, the latent diffusion model (L-diffusion) and parametric topological noise reduction network (PTDNet) were introduced as view generators to improve the limited representation ability and mitigate noise. Second, dual-view contrastive learning was constructed to alleviate the limitations of high-quality data in the recommendation system and the problem of model collapse in the training process. Third, a hard negative sampling strategy was proposed to improve the self-supervised signal. We extensively compared our method with 14 popular baselines on four public datasets (Yelp, BeerAdvocate, Gowalla, and LastFM). Experiments show an improvement of recommendation quality (e.g., that on the BeerAdocate dataset, NDCG@40 is improved by 3.6% and Recall@40 is improved by 3.3% on BeerAdvocate dataset).

We study emerging traits of interpersonal and social trust in online social networks of needs (OSNNs), where trust interactions start online and evolve into in-person meetings. We present a lightweight web scraping solution to harness data from online social networks; thanks to it we were able to monitor a nation-wide portal for childcare and see the evolution of online reviews from both families and carers. We analysed the data by first considering topological information to test centrality metrics as proxies for trustworthiness. Next, we focused on features/profile analysis and tested the Castelfranchi–Falcone trust model from psychology (CF-T), fitting it to online reviews of childcare services. Even though such reviews are relatively scarce and seemingly skewed, we feature-engineered the CF-T model to predict the evolution of reviews, treated as proxies for trust. By aggregating CF-T scores at the regional level, we discovered a strong correlation with per capita GDP, which suggests that high levels of trust in social networks of needs reflect social capital.

Abstract The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks, i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies.

Abstract Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations.

Abstract Previous key node identification approaches assume that the transmission of information on a path always ends positively, which is not necessarily true. In this paper, we propose a new centrality index called Information Rank (IR for short) that associates each path with a score specifying the probability that such path successfully conveys a message. The IR method generates all the shortest paths of any arbitrary length coming out from a node $u$ and defines the centrality of u as the sum of the scores of all the shortest paths exiting $u$. The IR algorithm is more robust than other centrality indexes based on shortest paths because it uses alternative paths in its computation, and it is computationally efficient because it relies on a Beadth First Search-BFS to generate all shortest paths. We validated the IR algorithm on nine real networks and compared its ability to identify super-spreaders (i.e. nodes capable of spreading an infection in a real network better than others) with five popular centrality indices such as Degree, Betweenness, K-Shell, DynamicRank and PageRank. Experimental results highlight the clear superiority of IR over all considered competitors.

Traditional community detection models either ignore the feature space information and require a large amount of domain knowledge to define the meta-paths manually, or fail to distinguish the importance of different meta-paths. To overcome these limitations, we propose a novel heterogeneous graph community detection method (called KGNN_HCD, heterogeneous graph Community Detection method based on K-nearest neighbor Graph Neural Network). Firstly, the similarity matrix is generated to construct the topological structure of K-nearest neighbor graph; secondly, the meta-path information matrix is generated using a meta-path transformation layer (Mp-Trans Layer) by adding weighted convolution; finally, a graph convolutional network (GCN) is used to learn high-quality node representation, and the k-means algorithm is adopted on node embeddings to detect the community structure. We perform extensive experiments and on three heterogeneous datasets, ACM, DBLP and IMDB, and we consider as competitors 11 community detection methods such as CP-GNN and GTN. The experimental results show that the proposed KGNN_HCD method improves 2.54% and 2.56% on the ACM dataset, 2.59% and 1.47% on the DBLP dataset, and 1.22% and 1.67% on the IMDB dataset for both NMI and ARI. Experiments findings suggest that the proposed KGNN_HCD method is reasonable and effective, and KGNN_HCD can be applied to complex network classification and clustering tasks.

With the development of deep learning and other technologies, the research of information propagation prediction has also achieved important research achievements. However, the existing information diffusion studies either focus on the attention relationships of users or they predict the information according to the diffusion relationships of users, which makes the prediction results have certain limitations. Therefore, a prediction model has been proposed spatial–temporal attention heterogeneous graph convolutional networks (STAHGCNs). First, we use GCN to learn user influence relationships and user behavior relationships, and we propose a user representation fusion mechanism to learn the user characteristics. Second, to account for the dynamics of user behavior, a temporal attention mechanism strategy is used to encode time into the heterogeneous graph to obtain a more expressive user representation. Finally, the obtained user representation is input into the multihead attention mechanism for information propagation prediction. Experimental results performed on the Twitter, Douban, Digg, and Memetracker datasets have shown that the proposed STAHGCN model increased by 8.80% and 6.74% at hits@N and map@N, respectively, which are significantly better than the original latest DyHGCN model. The proposed STAHGCN model effectively integrates spatial factors, such as time factor, user influence, and behavior, which greatly improves the accuracy of information propagation prediction and has great significance for rumor monitoring and malicious account detection.

Abstract Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of adversarial samples. A novel target-specific sentiment classification method is proposed. Firstly, the method of masking target entities is applied to replace synonyms and insert words randomly; secondly, the target-specific sentiment classification model of adversarial learning is constructed with six baseline models; finally, we combine data enhancement and adversarial learning to construct target-specific sentiment classification model. Experimental results show that Macro-F1 values are improved by 0.30–2.91, 0.88–2.42 and 0.13–1.94% compared to the six baseline models by using Laptop14, Restaurant14 and Twitter original datasets, respectively, using Adversarial learning. Using word-masking data enhancement samples and Adversarial learning from Laptop14, Restaurant14 and Twitter shows that Macro-F1 values are improved by 0.9–2.64, 1.59–3.09 and 0.18–1.71% compared to the six baseline (SC), respectively. Our method can effectively improve the quality of samples, it improves the classification performance and the capability of adversarial samples defense.

Branching processes are discrete-time stochastic processes which have been largely employed to model and simulate information diffusion processes over large online social networks such as Twitter and Reddit. Here we show that a variant of the branching process model enables the prediction of the popularity of user-generated content and thus can serve as a method for ranking search results or suggestions displayed to users. The proposed branching-process variant is able to evaluate the importance of an agent in a social network and, thus we propose a novel centrality index, called the Stochastic Potential Gain (SPG). The SPG is the first centrality index which combines the knowledge of the network topology with a dynamic process taking place on it which we call a graph-driven branching process. SPG generalises a range of popular network centrality metrics such as Katz' and Subgraph. We formulate a Monte Carlo algorithm (called MCPG) to compute the SPG and prove that it is convergent and correct. Experiments on two real datasets drawn from Facebook and GitHub demonstrate that MCPG traverses only a small fraction of nodes to produce its result, thus making the Stochastic Potential Gain an appealing option to compute node centrality measure for Online social networks.

In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is used to decompose the network, and the network is regarded as a hierarchical structure from the inner core to the periphery core. Second, the existing centrality methods such as H-index are used as the secondary score of the proposed method to select nodes in each hierarchy of the network. In addition, for the sake of alleviating the overlapping problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced to calculate the comprehensive score of secondary score and overlapping range, and the node with the highest comprehensive score will be selected in each round. The proposed algorithm can be used as a general framework to improve the existing centrality method which can represent nodes with definite values of centrality. Experimental results show that in the susceptible–infected–recovered (SIR) model experiment, compared with the benchmark methods, the infection scale of the proposed K-TOPSIS method in nine real networks is improved by 1.15%, 2.23%, 1.95%, 3.12%, 6.29%, −0.37%, 4.01%, 0.48%, and 0.48%, respectively. The novel method is improved by 0.44, 1.18, 1.16, 11.30, 2.03, 2.53, 2.70, and 2.13 in average shortest path length experiment, respectively, except for Facebook network. It shows that the novel method is reasonable and effective.

Social Network Analysis (SNA) is an interdisciplinary science that focuses on discovering the patterns of individuals interactions. In particular, practitioners have used SNA to describe and analyze criminal networks to highlight subgroups, key actors, strengths and weaknesses in order to generate disruption interventions and crime prevention systems. In this paper, the effectiveness of a total of seven disruption strategies for two real Mafia networks is investigated adopting SNA tools. Three interventions targeting actors with a high level of social capital and three interventions targeting those with a high human capital are put to the test and compared between each other and with random node removal. Similar tests on artificial model networks have also been carried out. Simulations show that actor removal based on social capital proves to be the most effective strategy, by leading to the total disruption of the criminal network in the least number of steps. The removal of a specific figure of a Mafia family such as the Caporegime seems also promising in the network disruption.

Abstract The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently) where a million-node ANN was trained on non-specialized laptops. Encouraged by those results, our study is now focused on some tunable parameters, to pursue a further acceleration effect. We show that, although optimal parameter tuning is unfeasible, due to the high non-linearity of ANN problems, we can actually come up with a set of useful guidelines that lead to speed-ups in practical cases. We find that significant reductions in execution time can generally be achieved by setting the revised fraction parameter ( $$\zeta $$ ζ ) to relatively low values.

Centrality metrics are a popular tool in Network Science to identify important nodes within a graph. We introduce the Potential Gain as a centrality measure that unifies many walk-based centrality metrics in graphs and captures the notion of node navigability, interpreted as the property of being reachable from anywhere else (in the graph) through short walks. Two instances of the Potential Gain (called the Geometric and the Exponential Potential Gain) are presented and we describe scalable algorithms for computing them on large graphs. We also give a proof of the relationship between the new measures and established centralities. The geometric potential gain of a node can thus be characterized as the product of its Degree centrality by its Katz centrality scores. At the same time, the exponential potential gain of a node is proved to be the product of Degree centrality by its Communicability index. These formal results connect potential gain to both the “popularity” and “similarity” properties that are captured by the above centralities.

Navigability is a distinctive features of graphs associated with artificial or natural systems whose primary goal is the transportation of information or goods. We say that a graph is navigable when an agent is able to efficiently reach any target node in by means of local routing decisions. In a social network navigability translates to the ability of reaching an individual through personal contacts. Graph navigability is well-studied, but a fundamental question is still open: why are some individuals more likely than others to be reached via short, friend-of-a-friend, communication chains? In this article we answer the question above by proposing a novel centrality metric called the potential gain, which, in an informal sense, quantifies the easiness at which a target node can be reached. We define two variants of the potential gain, called the geometric and the exponential potential gain, and present fast algorithms to compute them. The geometric and the potential gain are the first instances of a novel class of composite centrality metrics, i.e., centrality metrics which combine the popularity of a node in G with its similarity to all other nodes. As shown in previous studies, popularity and similarity are two main criteria which regulate the way humans seek for information in large networks such as Wikipedia. We give a formal proof that the potential gain of a node is always equivalent to the product of its degree centrality (which captures popularity) and its Katz centrality (which captures similarity).

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations in Online Social Networks (OSNs). Our algorithm maps each OSN user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and it computes the trust a user i places in a user j as the dot product of trustor profile of i and the trustee profile of j . The PTP-MF algorithm incorporates also biases in trustor and trustee behaviour to make more accurate predictions. Experiments on four real-life datasets indicate that the PTP-MF algorithm significantly outperforms other methods in accuracy and it showcases a high scalability.

Graph robustness-the ability of a graph to preserve its connectivity after the loss of nodes and edges-has been extensively studied to quantify how social, biological, physical, and technical systems withstand to external damages. In this paper, we prove that graph robustness can be quickly estimated through the Randic index, a parameter introduced in chemistry to study organic compounds. We prove that Erdos-Renyj (ER) graphs are a good specimen of robust graphs because they lack of a clear modular structure; we derive an analytical expression for the Randic index of ER graphs and use ER graphs as an effective term of comparison to decide about graph robustness. Experiments on real datasets from different domains (scientific collaboration networks, content-sharing systems, co-purchase networks from an e-commerce platform, and a road network) show that real-life large graphs are more robust than ER ones with the same number of nodes and edges. We also observe that if node degree distribution closely follows a power law, then few edges contribute for more than half of the Randic index, thus indicating that the selective removal of those edges has devastating impact on graph robustness. Finally, we describe sampling-based algorithms to efficiently but accurately approximate the Randic index.

Detecting communities in graphs is a fundamental tool to understand the structure of Web-based systems and predict their evolution. Many community detection algorithms are designed to processundirected graphs(i.e., graphs with bidirectional edges) but many graphs on the Web-e.g., microblogging Web sites, trust networks or the Web graph itself-are oftendirected. Few community detection algorithms deal with directed graphs but we lack their experimental comparison. In this paper we evaluated some community detection algorithms across accuracy and scalability. A first group of algorithms (Label Propagation and Infomap) are explicitly designed to manage directed graphs while a second group (e.g., WalkTrap) simply ignores edge directionality; finally, a third group of algorithms (e.g., Eigenvector) maps input graphs onto undirected ones and extracts communities from the symmetrized version of the input graph. We ran our tests on both artificial and real graphs and, on artificial graphs, WalkTrap achieved the highest accuracy, closely followed by other algorithms; Label Propagation has outstanding performance in scalability on both artificial and real graphs. The Infomap algorithm showcased the best trade-off between accuracy and computational performance and, therefore, it has to be considered as a promising tool for Web Data Analytics purposes.

In collaborative Web-based platforms, user reputation scores are generally computed according to two orthogonal perspectives: (a) helpfulness-based reputation (HBR) scores and (b) centrality-based reputation (CBR) scores. In HBR approaches, the most reputable users are those who post the most helpful reviews according to the opinion of the members of their community. In CBR approaches, a “who-trusts-whom” network—known as a trust network —is available and the most reputable users occupy the most central position in the trust network, according to some definition of centrality. The identification of users featuring large HBR scores is one of the most important research issue in the field of Social Networks, and it is a critical success factor of many Web-based platforms like e-marketplaces, product review Web sites, and question-and-answering systems. Unfortunately, user reviews/ratings are often sparse, and this makes the calculation of HBR scores inaccurate. In contrast, CBR scores are relatively easy to calculate provided that the topology of the trust network is known. In this article, we investigate if CBR scores are effective to predict HBR ones, and, to perform our study, we used real-life datasets extracted from CIAO and Epinions (two product review Web sites) and Wikipedia and applied five popular centrality measures—Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank and Eigenvector Centrality—to calculate CBR scores. Our analysis provides a positive answer to our research question: CBR scores allow for predicting HBR ones and Eigenvector Centrality was found to be the most important predictor. Our findings prove that we can leverage trust relationships to spot those users producing the most helpful reviews for the whole community.

Crowd-sourcing has become a popular form of computer mediated collaborative work and OpenStreetMap represents one of the most successful crowd-sourcing systems, where the goal of building and maintaining an accurate global map of the world is being accomplished by means of contributions made by over 1.2M citizens. However, within this apparently large crowd, a tiny group of highly active users is responsible for the mapping of almost all the content. One may thus wonder to what extent the information being mapped is biased towards the interests and agenda of this group of users. In this paper, we present a method to quantitatively measure content bias in crowd-sourced geographic information. We then apply the method to quantify content bias across a three-year period of OpenStreetMap mapping in 40 countries. We find almost no content bias in terms of what is being mapped, but significant geographic bias; furthermore, we find that bias in terms of meticulousness varies with culture.

Summary In this paper, a distributed approach aimed at improving the quality of service in dynamic grid federations is presented. Virtual organizations (VO) are grouped into large‐scale federations in which the original goals and scheduling mechanisms are left unchanged, while grid nodes can be quickly instructed to join or leave any VO at any time. Moreover, an agent‐oriented framework is designed to observe and characterize past behaviors of nodes in terms of resource sharing and consumption, as well as to determine the trust relationships occurring between each pair of nodes. By combining trust and historical behaviors into a unified convenience measure, software agents are able to evaluate the (i) advantages of node's membership with VOs and (ii) whether a specific set of nodes is able to meet the actual requirements, in terms of resource sharing and consumption of a specific VO. The convenience measure has been exploited to design a fully decentralized, greedy procedure, aimed at controlling the grid formation process. Extensive simulations have shown that the coordinated and decentralized process of grid formation provides a powerful means to improve the overall quality of service of the grid federation. Copyright © 2015 John Wiley & Sons, Ltd.

Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is particularly relevant. While the literature usually refers to compactness as a measure to merely determine how much members of a group are similar among each other, we argue that the mutual trustworthiness between the members should be considered as an important factor in defining such a term. In fact, trust has profound effects on the dynamics of group formation and their evolution: individuals are more likely to join with and stay in a group if they can trust other group members. In this paper, we propose a quantitative measure of group compactness that takes into account both the similarity and the trustworthiness among users, and we present an algorithm to optimize such a measure. We provide empirical results, obtained from the real social networks EPINIONS and CIAO, that compare our notion of compactness versus the traditional notion of user similarity, clearly proving the advantages of our approach.

Modern online social platforms allow their members to be involved in a broad range of activities including getting friends, joining groups, posting, and commenting resources. In this paper, we investigate whether a correlation emerges across the different activities a user can take part in. For our analysis, we focused on aNobii, a social platform with a world-wide user base of book readers, who post their readings, give ratings, review books, and discuss them with friends and fellow readers. aNobii presents a heterogeneous structure: 1) part social network, with user-to-user interactions; 2) part interest network, with the management of book collections; and 3) part folksonomy, with books that are tagged by the users. We analyzed a complete snapshot of aNobii and we focused on three specific activities a user can perform, namely tagging behavior, tendency to join groups and aptitude to compile a wishlist of the books one is planning to read. For each user, we create a tag-based, a group-based, and a wishlist-based profile. Experimental analysis, which was carried out with information-theory tools like entropy and mutual information, suggests that tag-based and group-based profiles are in general more informative than wishlist-based ones. Furthermore, we discover that the degree of correlation between the three profiles associated with the same user tend to be small. Hence, user profiling cannot be reduced to considering just any one type of user activity (albeit important) but it is crucial to incorporate multiple dimensions to effectively describe users' preferences and behavior.

In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.

In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the κ-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.

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

In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information according to the directives of HL7, the information management standard adopted in medical context. Our system builds a profile for each patient and uses it to detect Healthcare Service Providers delivering e-health services potentially capable of satisfying his needs. In order to handle this search it can exploit three different algorithms: the first, called PPB, uses only information stored in the patient profile; the second, called DS-PPB, considers both information stored in the patient profile and similarities among the e-health services delivered by the involved providers; the third, called AB, relies on {\rm A}{\bf^*}, a popular search algorithm in Artificial Intelligence. Our system builds also a social network of patients; once a patient submits a query and retrieves a set of services relevant to him, our system applies a spreading activation technique on this social network to find other patients who may benefit from these services.

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.

In this paper, we present a multiagent system aiming at assisting citizens in the current e-government scenario characterized by a huge amount of heterogeneous services that makes it difficult to quickly answer citizen queries. We show that the adoption of intelligent agent technology makes our system capable of helping a citizen in his search of services in such a way as to satisfy his interests and to face his needs; moreover, this technology ensures a high level of proactivity because it can identify services potentially relevant to a citizen even though he has never required them explicitly; finally, it can enhance citizen participation to decisional processes because it can encourage citizens to form communities who can debate in such a way as to propose the activation of new services of interest to them.

In this paper, we propose an Extensible Markup Language (XML)-based multiagent recommender system for supporting online recruitment services. Our system is characterized by the following features: 1) it handles user profiles for personalizing the job search over the Internet; 2) it is based on the intelligent agent technology; and 3) it uses XML for guaranteeing a light, versatile, and standard mechanism for information representation, storing, and exchange. This paper discusses the basic features of the proposed system, presents the results of an experimental study we have carried out for evaluating its performance, and makes a comparison between the proposed system and other e-recruitment systems already presented in the past.

A large amount of research is currently focusing on the issue of the adaptive control of the quality-of-service (QoS) provided to multimedia applications in heterogeneous wireless systems. In this paper, the authors aim at contributing to this issue by proposing a mechanism that exploits user profiling techniques and suitable QoS mapping functions to introduce the soft QoS idea into a wireless multimedia scenario. The research objective is a QoS control architecture, which enables the continuous convergence between the actual user preferences and expectations and the resource constraints of the underlying wireless system. The proposed architecture operates between the system and the application layer. This allows it to achieve the intended results, by means of an effective dynamic reconfiguration of the applications and the contemporary renegotiation of the wireless resources.