Seeds Buffering for Information Spreading Processes

Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.Comment: Jankowski, J., Br\'odka, P., Michalski, R., & Kazienko, P. (2017, September). Seeds Buffering for Information Spreading Processes. In International Conference on Social Informatics (pp. 628-641). Springe

Probing Limits of Information Spread with Sequential Seeding

We consider here information spread which propagates with certain probability from nodes just activated to their not yet activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. Such activation is commonly performed in a single stage. A novel approach based on sequential seeding is analyzed here resulting in three fundamental contributions. First, we propose a coordinated execution of randomized choices to enable precise comparison of different algorithms in general. We apply it here when the newly activated nodes at each stage of spreading attempt to activate their neighbors. Then, we present a formal proof that sequential seeding delivers at least as large coverage as the single stage seeding does. Moreover, we also show that, under modest assumptions, sequential seeding achieves coverage provably better than the single stage based approach using the same number of seeds and node ranking. Finally, we present experimental results showing how single stage and sequential approaches on directed and undirected graphs compare to the well-known greedy approach to provide the objective measure of the sequential seeding benefits. Surprisingly, applying sequential seeding to a simple degree-based selection leads to higher coverage than achieved by the computationally expensive greedy approach currently considered to be the best heuristic

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table

Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8\% and 3.0\% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9\% and 16.5\% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance.Comment: Accepted by IP&

Information Evolution in Complex Networks

Many biological phenomena or social events critically depend on how information evolves in complex networks. A seeming paradox of the information evolution is the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. The current research pursues to understand the underlying mechanisms of such coexistence. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but the noise accounts for their intensification. We further demonstrate the ubiquity of our discovered laws by applying them to analyze the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks

Digital vernetztes Handeln verstehen: Eine Fallstudie zu #HomeToVote und dem irischen Abtreibungsreferendum 2018

Digitally networked action (Bennett & Segerberg, 2012) has become a prominent political reality. This article explores the evolution of digitally networked action, considering the Twitter hashtag #HomeToVote in 2018 as a relevant case. The case study features the return of Irish expatriates to their home country to vote in the referendum on abortion rights, since no postal votes were available to Irish citizens abroad. We investigated how actors participated in digitally networked action on Twitter, viewed from three perspectives: composition, diffusion, and dynamics. Through an @-mention network with 7,373 edges and 5,198 nodes, built on all original tweets (N = 33,927) about #HomeToVote, we interpreted the digitally networked action based on social interaction and information distribution between and beyond categorized subgroups of actors during four phases. The early phases of #HomeToVote are related to engagement and mobilization, while the latter phases are associated with experience sharing and solidarity declaration. Throughout the development of #HomeToVote, individuals and organizational actors show collective endeavors to promote digitally networked action, while media actors use Twitter to consistently depict moments of #HomeToVote. The findings suggest that #HomeToVote, as an organizationally enabled advocacy network, has a large political capacity to share communication linkages, facilitate flexible affiliations, and employ personalized engagement mechanisms.Dieser Artikel untersucht den Twitter-Hashtag #HomeToVote im Jahr 2018 als relevanten Fall der Entwicklung der „digitally networked action“ (Bennett & Segerberg, 2012). In der Fallstudie geht es um die Rückkehr irischer Auswanderer in ihr Heimatland, um an dem Referendum über Abtreibungsrechte teilzunehmen, da irischen Bürger*innen im Ausland keine Briefwahl möglich war. Wir untersuchten, wie Akteure an der „digitally networked action“ auf Twitter teilnahmen, aus drei Perspektiven: Zusammensetzung, Diffusion und Dynamik. Anhand eines @-mention-Netzwerks mit 7.373 Kanten und 5.198 Knoten, das auf allen Original-Tweets (N = 33.927) zum Thema #HomeTo-Vote aufgebaut wurde, interpretierten wir die „digitally networked action“ anhand der sozialen Interaktion und Informationsverteilung zwischen kategorisierten Untergruppen von Akteuren innerhalb von vier Phasen. Die frühen Phasen von #HomeToVote stehen im Zusammenhang mit Engagement und Mobilisierung, während die späteren Phasen mit Erfahrungsaustausch und Solidaritätserklärungen verbunden sind. Während der gesamten Entwicklung von #HomeToVote zeigen Individuen und organisatorische Akteure kollektive Bemühungen, um „digitally networked action“ zu fördern, während Medienakteure Twitter nutzen, um Momente von #HomeToVote konsistent darzustellen. Die Ergebnisse deuten darauf hin, dass #HomeToVote als organisatorisch ermöglichtes Advocacy-Netzwerk eine große politische Kapazität hat, um Kommunikationsverbindungen zu teilen, flexible Zugehörigkeiten zu erleichtern und personalisierte Engagement-Mechanismen zu ermöglichen