Improving information centrality of a node in complex networks by adding edges

The problem of increasing the centrality of a network node arises in many practical applications. In this paper, we study the optimization problem of maximizing the information centrality $I_v$ of a given node $v$ in a network with $n$ nodes and $m$ edges, by creating $k$ new edges incident to $v$. Since $I_v$ is the reciprocal of the sum of resistance distance $\mathcal{R}_v$ between $v$ and all nodes, we alternatively consider the problem of minimizing $\mathcal{R}_v$ by adding $k$ new edges linked to $v$. We show that the objective function is monotone and supermodular. We provide a simple greedy algorithm with an approximation factor $\left(1-\frac{1}{e}\right)$ and $O(n^3)$ running time. To speed up the computation, we also present an algorithm to compute $\left(1-\frac{1}{e}-\epsilon\right)$-approximate resistance distance $\mathcal{R}_v$ after iteratively adding $k$ edges, the running time of which is $\widetilde{O} (mk\epsilon^{-2})$ for any $\epsilon>0$, where the $\widetilde{O} (\cdot)$ notation suppresses the ${\rm poly} (\log n)$ factors. We experimentally demonstrate the effectiveness and efficiency of our proposed algorithms.Comment: 7 pages, 2 figures, ijcai-201

Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways

Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequently receive recommendations which may steer her towards more and more radicalized content, eventually being trapped in what we call a "radicalization pathway". In this paper, we study the problem of mitigating radicalization pathways using a graph-based approach. Specifically, we model the set of recommendations of a "what-to-watch-next" recommender as a d-regular directed graph where nodes correspond to content items, links to recommendations, and paths to possible user sessions. We measure the "segregation" score of a node representing radicalized content as the expected length of a random walk from that node to any node representing non-radicalized content. High segregation scores are associated to larger chances to get users trapped in radicalization pathways. Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations. We prove that the problem of finding the optimal set of recommendations to rewire is NP-hard and NP-hard to approximate within any factor. Therefore, we turn our attention to heuristics, and propose an efficient yet effective greedy algorithm based on the absorbing random walk theory. Our experiments on real-world datasets in the context of video and news recommendations confirm the effectiveness of our proposal.Peer reviewe

Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics

The operation of creating edges has been widely applied to optimize relevant quantities of opinion dynamics. In this paper, we consider a problem of polarization optimization for the leader-follower opinion dynamics in a noisy social network with $n$ nodes and $m$ edges, where a group $Q$ of $q$ nodes are leaders, and the remaining $n-q$ nodes are followers. We adopt the popular leader-follower DeGroot model, where the opinion of every leader is identical and remains unchanged, while the opinion of every follower is subject to white noise. The polarization is defined as the steady-state variance of the deviation of each node's opinion from leaders' opinion, which equals one half of the effective resistance $\mathcal{R}_Q$ between the node group $Q$ and all other nodes. Concretely, we propose and study the problem of minimizing $\mathcal{R}_Q$ by adding $k$ new edges with each incident to a node in $Q$. We show that the objective function is monotone and supermodular. We then propose a simple greedy algorithm with an approximation factor $1-1/e$ that approximately solves the problem in $O((n-q)^3)$ time. To speed up the computation, we also provide a fast algorithm to compute (1-1/e-\eps)-approximate effective resistance $\mathcal{R}_Q$, the running time of which is \Otil (mk\eps^{-2}) for any \eps>0, where the \Otil (\cdot) notation suppresses the ${\rm poly} (\log n)$ factors. Extensive experiment results show that our second algorithm is both effective and efficient.Comment: This paper has been accepted in CIKM'23 conferenc

RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions

The topology of the hyperlink graph among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a polarized bubble with no access to other opinions. We model readers' behavior as random walks. A node is in a polarized bubble if the expected length of a random walk from it to a page of different opinion is large. The structural bias of a graph is the sum of the radii of highly-polarized bubbles. We study the problem of decreasing the structural bias through edge insertions. Healing all nodes with high polarized bubble radius is hard to approximate within a logarithmic factor, so we focus on finding the best $k$ edges to insert to maximally reduce the structural bias. We present RePBubLik, an algorithm that leverages a variant of the random walk closeness centrality to select the edges to insert. RePBubLik obtains, under mild conditions, a constant-factor approximation. It reduces the structural bias faster than existing edge-recommendation methods, including some designed to reduce the polarization of a graph

RELISON: A Framework for Link Recommendation in Social Networks

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON

Computational approaches for engineering effective teams

The performance of a team depends not only on the abilities of its individual members, but also on how these members interact with each other. Inspired by this premise and motivated by a large number of applications in educational, industrial and management settings, this thesis studies a family of problems, known as team-formation problems, that aim to engineer teams that are effective and successful. The major challenge in this family of problems is dealing with the complexity of the human team participants. Specifically, each individual has his own objectives, demands, and constraints that might be in contrast with the desired team objective. Furthermore, different collaboration models lead to different instances of team-formation problems. In this thesis, we introduce several such models and describe techniques and efficient algorithms for various instantiations of the team-formation problem. This thesis consists of two main parts. In the first part, we examine three distinct team-formation problems that are of significant interest in (i) educational settings, (ii) industrial organizations, and (iii) management settings respectively. What constitutes an effective team in each of the aforementioned settings is totally dependent on the objective of the team. For instance, the performance of a team (or a study group) in an educational setting can be measured as the amount of learning and collaboration that takes place inside the team. In industrial organizations, desirable teams are those that are cost-effective and highly profitable. Finally in management settings, an interesting body of research uncovers that teams with faultlines are prone to performance decrements. Thus, the challenge is to form teams that are free of faultlines, that is, to form teams that are robust and less likely to break due to disagreements. The first part of the thesis discusses approaches for formalizing these problems and presents efficient computational methods for solving them. In the second part of the thesis, we consider the problem of improving the functioning of existing teams. More precisely, we show how we can use models from social theory to capture the dynamics of the interactions between the team members. We further discuss how teams can be modified so that the interaction dynamics lead to desirable outcomes such as higher levels of agreement or lesser tension and conflict among the team members

Analysis, Modeling, and Control of Dynamic Processes in Networks

Dynamic network processes have surrounded people for millennia. Information spread through social networks, alliance formation in financial and organizational networks, heat diffusion through material networks, and distributed synchronization in robotic networks are just a few examples. Network processes are studies along three dimensions: analysis of network processes through the data produced by them; designing complex plausible, yet, tractable mathematical models for network processes; and designing control mechanisms that would guide network processes towards desirable evolution patterns. This thesis advances the frontier of knowledge about network processes along each of these three dimensions, emphasizing applications to social networks.The first part of the thesis is dedicated to the design of a method for model-driven analysis of a polar opinion formation process in social networks. The core of the method is a distance measure quantifying the likelihood of a social network's transitioning between different states with respect to a chosen opinion dynamics model characterizing expected evolution of the network's state. I describe how to design such a distance measure relying upon the classical transportation problem, compute it in linear time, and use it in applications.In the second part of the thesis, I focus on designing a model for polar opinion formation in social networks, and define a class of non-linear models that capture the dependence of the users' opinion formation behavior upon the opinions themselves. The obtained models are connected to socio-psychological theories, and their behavior is theoretically analyzed employing tools from non-smooth analysis and a generalization of LaSalle Invariance Principle.The third part of the thesis targets the problem of defense against social control. While the existing socio-psychological theories as well as influence maximization techniques expose the opinion formation process in social networks to external attacks, I propose an algorithm that nullifies the effect of such attacks by strategically recommending a small number of new edges to the network's users. The optimization problem underlying the algorithm is NP-hard, and I provide a pseudo-linear time heuristic---drawing upon the theory of Markov chains---that solves the problem approximately and performs well in experiments

Contact Recommendation: Effects on the Evolution of Social Networks

En los últimas dos décadas y media, el desarrollo y crecimiento de los sistemas de recomendación ha progresado cada vez más rápido. Esta expansión ha dado lugar a la confluencia entre las tecnologías de recomendación y otras áreas adyacentes, y, en particular, con las tecnologías de redes sociales, que han experimentado un crecimiento exponencial en los últimos años. El presente trabajo explora uno de los problemas más novedosos que surgen de la confluencia entre ambas áreas: la recomendación de contactos en redes sociales. Nuestro trabajo se centra, por un lado, en obtener una perspectiva completa de la efectividad de una amplia selección de algoritmos de recomendación, incluyendo algunas contribuciones originales, y considerando perspectivas novedosas que van más allá del acierto de la recomendación. Por otro, en el estudio de la influencia que los algoritmos de recomendación de contactos ejercen sobre la evolución de las redes sociales y sus propiedades. Una fracción no despreciable de los nuevos enlaces que aparecen en las modernas redes sociales online (como Twitter, LinkedIn o Facebook) son creados a través de sugerencias de contactos personalizadas de la plataforma de red social. Los sistemas de recomendación están convirtiendose en un factor importante para influenciar la evolución de la red. Comprender mejor este efecto y aprovechar la oportunidad de obtener más beneficios de la acción de los recomendadores desde una perspectiva amplia de la red son, por tanto, direcciones de investigación que merece la pena investigar, y que estudiamos aquí. Nuestro estudio comprende trabajo teórico y algorítmico, incluyendo la definición y adaptación de métricas de evaluación novedosas. Esto lo complementamos con un exhaustivo trabajo experimental, en el que comparamos múltiples algoritmos de recomendación desarrollados en diferentes áreas, incluyendo la predicción de enlaces, los sistemas de recomendación clásicos y la recuperación de información, junto con otros algoritmos propios del campo de recomendación de contactos. Hemos evaluado los efectos en la evolución de las redes sociales mediante experimentos offline sobre varios grafos de la red social Twitter. Hemos considerado dos tipos de grafos: grafos de interacción entre usuarios (retweets, menciones y respuestas) y grafos de amistad explícitos (relaciones de follow). Con dichos experimentos, se ha medido no sólo el acierto de los recomendadores: también se han estudiado perspectivas más novedosas, como la novedad y diversidad de las recomendaciones, y sus efectos sobre las propiedades estructurales de la red. Finalmente, hemos analizado los efectos de promocionar ciertas métricas globales de diversidad estructural de las recomendaciones sobre el flujo de información que viaja a través de las redes, en términos de la velocidad de la difusión y de la diversidad de la información que reciben los usuarios.Over the last two and a half decades, the development and expansion of recommender systems has progressed increasingly fast. This expansion has given place to the confluence between recommendation technologies and other adjacent areas, notably social networks technologies, which have similarly experienced an exponential growth of their own in the last few years. This thesis explores one of the most novel problems arised from the confluence between both areas: the recommendation of contacts in social networks. Our work focuses, on one hand, on gaining a comprehensive perspective of the effectiveness of a wide range of recommendation algorithms including some of our own original contributions, and considering novel target perspectives beyond the recommendation accuracy. And on the other, on the study of the influence that contact recommendation algorithms have on the evolution of social networks and their properties. A non-negligible fraction of the new links between pairs of users in modern online social networks (such as Twitter, Facebook or LinkedIn) are created through personalized contacts suggestions made by the social network platform. Recommender systems are hence becoming an important factor influencing the evolution of the network. Better understanding this efffect, and taking advantage of the opportunity to draw further benefit from the action of recommenders with a broader network perspective, are therefore a worthwile research direction which we aim to undertake here. Our study comprises algorithmic and theoretical work, including the definition and adaptation of novel evaluation metrics. We complement this with extensive experimental work, where we start by comparing multiple recommendation algorithms developed in different areas including link prediction, classical recommender systems and text information retrieval along with other algorithms from the contact recommendation field. We have evaluated the effects over the evolution of social networks via offline experiments over several graphs extracted from the Twitter social network. Two different types of graphs have been considered: graphs which represent the different interactions between users (retweets, replies and mentions) and explicit graphs (follows relations). With those experiments, we have not only measured the accuracy of the recommendation algorithms, but also more novel perspectives such as the novelty and diversity of the recommendations, and their effects on the structural properties of the network. Finally, we have measured the effects of enhancing the structural diversity of the recommendation over the flow of information which travels through the network in terms of the speed of the diffusion and the diversity of the information received by the different users