scholarly journals Higher order monotonicity and submodularity of influence in social networks: From local to global

2022 ◽  
pp. 104864
Author(s):  
Wei Chen ◽  
Qiang Li ◽  
Xiaohan Shan ◽  
Xiaoming Sun ◽  
Jialin Zhang
Keyword(s):  
Social Networks ◽  
Higher Order

scholarly journals Evolutionary dynamics of higher-order interactions in social networks

2021 ◽  
Author(s):  
Unai Alvarez-Rodriguez ◽  
Federico Battiston ◽  
Guilherme Ferraz de Arruda ◽  
Yamir Moreno ◽  
Matjaž Perc ◽  
...  
Keyword(s):  
Social Networks ◽  
Evolutionary Dynamics ◽  
Higher Order

Enabling social networks in an R&D organization

2006 ◽  
Vol 25 (4) ◽  
pp. 237-246
Author(s):  
Tomas Hellström
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Qualitative Study ◽  
Network Formation ◽  
Higher Order ◽  
Practical Significance ◽  
Administrative Unit ◽  
High Level ◽  
Social Network Formation

This paper presents a qualitative study of mechanisms enabling social network formation in the R&D unit of a large technology-based organization. Drawing on interviews with 37 high-level technical and administrative unit members, a number of social network enablers could be discerned, which related to the need for effective location mechanisms, special “enrolment spaces”, and mechanisms for forging contacts. It was also possible to identify a number of higher-order factors for facilitation of network formation, namely hierarchical enablers and communicative and assimilative factors. Based on these results, the paper makes suggestions as to the theoretical and practical significance of social network enabling mechanisms in R&D organizations.

scholarly journals Bot Detection on Social Networks Using Persistent Homology

2020 ◽  
Vol 25 (3) ◽  
pp. 58
Author(s):  
Minh Nguyen ◽  
Mehmet Aktas ◽  
Esra Akbas
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Persistent Homology ◽  
Structural Features ◽  
Higher Order ◽  
Ego Networks ◽  
Feature Extraction Method ◽  
Topological Features ◽  
Bot Detection

The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.

9. Neighborhood-Based Models for Social Networks

2002 ◽  
Vol 32 (1) ◽  
pp. 301-337 ◽  
Author(s):  
Philippa Pattison ◽  
Garry Robins
Keyword(s):  
Social Networks ◽  
Conceptual Framework ◽  
Higher Order ◽  
Model Construction ◽  
Network Ties ◽  
Interactive Network ◽  
Complex Models ◽  
Long Cycles

We argue that social networks can be modeled as the outcome of processes that occur in overlapping local regions of the network, termed local social neighborhoods. Each neighborhood is conceived as a possible site of interaction and corresponds to a subset of possible network ties. In this paper, we discuss hypotheses about the form of these neighborhoods, and we present two new and theoretically plausible ways in which neighborhood-based models for networks can be constructed. In the first, we introduce the notion of a setting structure, a directly hypothesized (or observed) set of exogenous constraints on possible neighborhood forms. In the second, we propose higher-order neighborhoods that are generated, in part, by the outcome of interactive network processes themselves. Applications of both approaches to model construction are presented, and the developments are considered within a general conceptual framework of locale for social networks. We show how assumptions about neighborhoods can be cast within a hierarchy of increasingly complex models; these models represent a progressively greater capacity for network processes to “reach” across a network through long cycles or semipaths. We argue that this class of models holds new promise for the development of empirically plausible models for networks and network-based processes.

scholarly journals Individual Differences in Learning Social and Non-Social Network Structures

2017 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Individual Differences ◽  
Social Contexts ◽  
Higher Order ◽  
Social Orientation ◽  
Order Structure ◽  
Network Learning ◽  
Social Traits ◽  
The Impact

Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network structure. We examined individual differences in the ability to learn network topology for which stimuli were social versus non-social. Although participants were able to learn both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. Importantly, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social networks but not the learning of non-social networks. Taken together, our results suggest that the process of learning higher-order structure in social networks is independent from the process of learning higher-order structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes. Implications for how people learn and adapt to new social contexts that require integration into a new social network are discussed.

scholarly journals Temporal properties of higher-order interactions in social networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giulia Cencetti ◽  
Federico Battiston ◽  
Bruno Lepri ◽  
Márton Karsai
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Social Dynamics ◽  
Temporal Dynamics ◽  
Higher Order ◽  
Order Structure ◽  
Face To Face ◽  
Work Settings ◽  
Temporal Properties ◽  
Social Settings

AbstractHuman social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people. Such interactions, approximating face-to-face communications, can be effectively represented as time varying social networks with links being unceasingly created and destroyed over time. Traditional analyses of temporal networks have addressed mostly pairwise interactions, where links describe dyadic connections among individuals. However, many network dynamics are hardly ascribable to pairwise settings but often comprise larger groups, which are better described by higher-order interactions. Here we investigate the higher-order organizations of temporal social networks by analyzing five publicly available datasets collected in different social settings. We find that higher-order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics, with bursty trains of rapidly recurring higher-order events separated by long periods of inactivity. We investigate the evolution and formation of groups by looking at the transition rates between different higher-order structures. We find that in more spontaneous social settings, group are characterized by slower formation and disaggregation, while in work settings these phenomena are more abrupt, possibly reflecting pre-organized social dynamics. Finally, we observe temporal reinforcement suggesting that the longer a group stays together the higher the probability that the same interaction pattern persist in the future. Our findings suggest the importance of considering the higher-order structure of social interactions when investigating human temporal dynamics.

scholarly journals Evolution of honesty in higher-order social networks

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Aanjaneya Kumar ◽  
Sandeep Chowdhary ◽  
Valerio Capraro ◽  
Matjaž Perc
Keyword(s):  
Social Networks ◽  
Higher Order

COOPERATION, CONFLICT AND HIGHER-ORDER STRUCTURES OF SOCIAL NETWORKS

2012 ◽  
Vol 15 (supp01) ◽  
pp. 1250055 ◽  
Author(s):  
SLOBODAN MALETIĆ ◽  
DANIJELA HORAK ◽  
MILAN RAJKOVIĆ
Keyword(s):  
Social Networks ◽  
Spectral Analysis ◽  
Social Network ◽  
Simplicial Complexes ◽  
Higher Order ◽  
Laplacian Operator ◽  
Graph Spectra ◽  
Combinatorial Laplacian ◽  
Les Miserables ◽  
Order Structures

Simplicial complexes represent powerful models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes in the context of connectivity of cliques in the simplicial clique complex associated with social networks. The necessity of higher order spectral analysis is discussed and compared with results for ordinary graph spectra. Methods and results are applied using social network of the Zachary karate club and the network of characters from Victor Hugo's novel Les Miserables.

scholarly journals Event detection in temporal social networks using a higher-order network model

2021 ◽  
Vol 31 (11) ◽  
pp. 113144
Author(s):  
Xiang Li ◽  
Xue Zhang ◽  
Qizi Huangpeng ◽  
Chengli Zhao ◽  
Xiaojun Duan
Keyword(s):  
Social Networks ◽  
Network Model ◽  
Event Detection ◽  
Higher Order

scholarly journals Comparing social networks

2006 ◽  
Vol 3 (2) ◽  
Author(s):  
Katherine Faust
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Local Structure ◽  
Bird Species ◽  
Higher Order ◽  
Local Network ◽  
Isomorphism Classes ◽  
Dominance Relations ◽  
Empirical Base ◽  
Empirical Networks

This paper demonstrates limitations in usefulness of the triad census for studying similarities among local structural properties of social networks. A triad census succinctly summarizes the local structure of a network using the frequencies of sixteen isomorphism classes of triads (sub-graphs of three nodes). The empirical base for this study is a collection of 51 social networks measuring different relational contents (friendship, advice, agonistic encounters, victories in fights, dominance relations, and so on) among a variety of species (humans, chimpanzees, hyenas, monkeys, ponies, cows, and a number of bird species). Results show that, in aggregate, similarities among triad censuses of these empirical networks are largely explained by nodal and dyadic properties – the density of the network and distributions of mutual, asymmetric, and null dyads. These results remind us that the range of possible network-level properties is highly constrained by the size and density of the network and caution should be taken in interpreting higher order structural properties when they are largely explained by local network features.

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