Social and Information Networks

New submissions

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New submissions for Fri, 10 May 24

[1]  arXiv:2405.05275 [pdf, other]

Title: SoMeR: Multi-View User Representation Learning for Social Media

Authors: Siyi Guo, Keith Burghardt, Valeria Pantè, Kristina Lerman

Subjects: Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI)

User representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations have widespread applications in recommendation systems and advertising; however, existing methods typically rely on specific features like text content, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address this limitation, we propose SoMeR, a Social Media user Representation learning framework that incorporates temporal activities, text content, profile information, and network interactions to learn comprehensive user portraits. SoMeR encodes user post streams as sequences of timestamped textual features, uses transformers to embed this along with profile data, and jointly trains with link prediction and contrastive learning objectives to capture user similarity. We demonstrate SoMeR's versatility through two applications: 1) Identifying inauthentic accounts involved in coordinated influence operations by detecting users posting similar content simultaneously, and 2) Measuring increased polarization in online discussions after major events by quantifying how users with different beliefs moved farther apart in the embedding space. SoMeR's ability to holistically model users enables new solutions to important problems around disinformation, societal tensions, and online behavior understanding.

[2]  arXiv:2405.05288 [pdf, other]

Title: Learning Social Graph for Inactive User Recommendation

Authors: Nian Liu, Shen Fan, Ting Bai, Peng Wang, Mingwei Sun, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Chuan Shi

Comments: This paper has been received by DASFAA 2024

Subjects: Social and Information Networks (cs.SI); Machine Learning (cs.LG)

Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to their inferior quality and insufficient quantity, especially for inactive users, whose interacted items are limited. In this paper, we propose a novel social recommendation method called LSIR (\textbf{L}earning \textbf{S}ocial Graph for \textbf{I}nactive User \textbf{R}ecommendation) that learns an optimal social graph structure for social recommendation, especially for inactive users. LSIR recursively aggregates user and item embeddings to collaboratively encode item and user features. Then, graph structure learning (GSL) is employed to refine the raw user-user social graph, by removing noisy edges and adding new edges based on the enhanced embeddings. Meanwhile, mimic learning is implemented to guide active users in mimicking inactive users during model training, which improves the construction of new edges for inactive users. Extensive experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58\% on NDCG in inactive user recommendation. Our code is available at~\url{https://github.com/liun-online/LSIR}.

[3]  arXiv:2405.05393 [pdf, other]

Title: Mutual information and the encoding of contingency tables

Authors: Maximilian Jerdee, Alec Kirkley, M. E. J. Newman

Comments: 18 pages, 9 figures

Subjects: Social and Information Networks (cs.SI)

Mutual information is commonly used as a measure of similarity between competing labelings of a given set of objects, for example to quantify performance in classification and community detection tasks. As argued recently, however, the mutual information as conventionally defined can return biased results because it neglects the information cost of the so-called contingency table, a crucial component of the similarity calculation. In principle the bias can be rectified by subtracting the appropriate information cost, leading to the modified measure known as the reduced mutual information, but in practice one can only ever compute an upper bound on this information cost, and the value of the reduced mutual information depends crucially on how good a bound is established. In this paper we describe an improved method for encoding contingency tables that gives a substantially better bound in typical use cases, and approaches the ideal value in the common case where the labelings are closely similar, as we demonstrate with extensive numerical results.

[4]  arXiv:2405.05576 [pdf, other]

Title: LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks

Authors: Hao Ren, Jiaojiao Jiang

Subjects: Social and Information Networks (cs.SI); Information Retrieval (cs.IR); Networking and Internet Architecture (cs.NI)

As the calculation of centrality in complex networks becomes increasingly vital across technological, biological, and social systems, precise and scalable ranking methods are essential for understanding these networks. This paper introduces LayerPlexRank, an algorithm that simultaneously assesses node centrality and layer influence in multiplex networks using algebraic connectivity metrics. This method enhances the robustness of the ranking algorithm by effectively assessing structural changes across layers using random walk, considering the overall connectivity of the graph. We substantiate the utility of LayerPlexRank with theoretical analyses and empirical validations on varied real-world datasets, contrasting it with established centrality measures.

[5]  arXiv:2405.05724 [pdf, other]

Title: Private Online Community Detection for Censored Block Models

Authors: Mohamed Seif, Liyan Xie, Andrea J. Goldsmith, H. Vincent Poor

Subjects: Social and Information Networks (cs.SI); Cryptography and Security (cs.CR); Information Theory (cs.IT)

We study the private online change detection problem for dynamic communities, using a censored block model (CBM). Focusing on the notion of edge differential privacy (DP), we seek to understand the fundamental tradeoffs between the privacy budget, detection delay, and exact community recovery of community labels. We establish the theoretical lower bound on the delay in detecting changes privately and propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy. Further, we provide theoretical guarantees for the effectiveness of our proposed method by showing necessary and sufficient conditions on change detection and exact recovery under edge DP. Simulation and real data examples are provided to validate the proposed method.

Cross-lists for Fri, 10 May 24

[6]  arXiv:2405.05433 (cross-list from cs.MA) [pdf, other]

Title: Robust Reward Placement under Uncertainty

Authors: Petros Petsinis, Kaichen Zhang, Andreas Pavlogiannis, Jingbo Zhou, Panagiotis Karras

Comments: Accepted for publication in IJCAI 2024

Subjects: Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)

Reward placement is a common optimization problem in network diffusion processes, where a number of rewards are to be placed in a network so as to maximize the total reward obtained as agents move randomly in it. In many settings, the precise mobility network might be one of several possible, based on parameters outside our control, such as the weather conditions affecting peoples' transportation means. Solutions to the reward placement problem must thus be robust to this uncertainty, by achieving a high utility in all possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly on a Markovian Mobility Model that has a predetermined set of locations but its precise connectivity is unknown and chosen adversarialy from a known set $\Pi$ of candidates. Network optimization is achieved by selecting a set of reward states, and the goal is to maximize the minimum, among all candidates, ratio of rewards obtained over the optimal solution for each candidate. We first prove that RRP is NP-hard and inapproximable in general. We then develop $\Psi$-Saturate, a pseudo-polynomial time algorithm that achieves an $\epsilon$-additive approximation by exceeding the budget constraint by a factor that scales as $O(ln|\Pi|/\epsilon)$. In addition, we present several heuristics, most prominently one inspired from a dynamic programming algorithm for the max-min 0-1 Knapsack problem. We corroborate our theoretical findings with an experimental evaluation of the methods in both synthetic and real-world datasets.

Replacements for Fri, 10 May 24

[7]  arXiv:2211.06352 (replaced) [pdf, other]

Title: Spectral Triadic Decompositions of Real-World Networks

Authors: Sabyasachi Basu, Suman Kalyan Bera, C. Seshadhri

Subjects: Social and Information Networks (cs.SI); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS)

[8]  arXiv:2301.06774 (replaced) [pdf, other]

Title: Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Authors: Serena Tardelli, Leonardo Nizzoli, Maurizio Tesconi, Mauro Conti, Preslav Nakov, Giovanni Da San Martino, Stefano Cresci

Comments: Article published in PNAS 121 (20). Please, cite the published version

Journal-ref: Proceedings of the National Academy of Sciences 121 (20), e2307038121, 2024

Subjects: Social and Information Networks (cs.SI); Computation and Language (cs.CL); Computers and Society (cs.CY)

[9]  arXiv:2312.07077 (replaced) [pdf, other]

Title: On the Potential of an Independent Avatar to Augment Metaverse Social Networks

Authors: Theofanis P. Raptis, Chiara Boldrini, Marco Conti, Andrea Passarella

Comments: Supported by the projects: Piano Nazionale di Ripresa e Resilienza IR0000013 - "SoBigData.it", Partenariato Esteso PE00000013 - "FAIR", Centro Nazionale CN00000013 - "ICSC"

Journal-ref: IEEE ICCCN 2024

Subjects: Social and Information Networks (cs.SI); Human-Computer Interaction (cs.HC)

[10]  arXiv:2011.08069 (replaced) [pdf, other]

Title: Reconciling Security and Utility in Next-Generation Epidemic Risk Mitigation Systems

Authors: Pierfrancesco Ingo, Nichole Boufford, Ming Cheng Jiang, Rowan Lindsay, Matthew Lentz, Gilles Barthe, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Deepak Garg, Peter Druschel, Aastha Mehta

Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Social and Information Networks (cs.SI); Populations and Evolution (q-bio.PE)

[11]  arXiv:2203.07678 (replaced) [pdf, other]

Title: Incorporating Heterophily into Graph Neural Networks for Graph Classification

Authors: Jiayi Yang, Sourav Medya, Wei Ye

Comments: 8 pages

Subjects: Machine Learning (cs.LG); Social and Information Networks (cs.SI)

[12]  arXiv:2308.13604 (replaced) [pdf, other]

Title: Network science Ising states of matter

Authors: Hanlin Sun, Rajat Kumar Panda, Roberto Verdel, Alex Rodriguez, Marcello Dalmonte, Ginestra Bianconi

Comments: 17 pages, 18 figures

Subjects: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Social and Information Networks (cs.SI); Data Analysis, Statistics and Probability (physics.data-an); Physics and Society (physics.soc-ph)

• New submissions
• Cross-lists
• Replacements