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.

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.

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.

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 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 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.