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

Author(s):  
Unai Alvarez-Rodriguez ◽  
Federico Battiston ◽  
Guilherme Ferraz de Arruda ◽  
Yamir Moreno ◽  
Matjaž Perc ◽  
...  
2006 ◽  
Vol 25 (4) ◽  
pp. 237-246
Author(s):  
Tomas Hellström

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.


Author(s):  
Masha Etkind ◽  
Ron S. Kenett ◽  
Uri Shafrir

In this chapter we describe a novel pedagogy for conceptual thinking and peer cooperation with Meaning Equivalence Reusable Learning Objects (MERLO) that enhances higher-order thinking; deepen comprehension of conceptual content; and improves learning outcomes. The evolution of this instructional methodology follows insights from four recent developments: analysis of patterns of content and structure of labeled patterns in human experience, that led to the emergence of concept science; development of digital cyber-infrastructure of networked information; research in neuroscience and brain imaging, showing that exposure of learners to multi-semiotic inductive problems enhance cognitive control of inter-hemispheric attentional processing in the lateral brain, and increase higher-order thinking; research in evolutionary dynamics on peer cooperation and indirect reciprocity, that document the motivational effect of knowledge of being observed, a psychological imperative that motivate individuals to cooperate and to contribute to the common good.


2020 ◽  
Vol 25 (3) ◽  
pp. 58
Author(s):  
Minh Nguyen ◽  
Mehmet Aktas ◽  
Esra Akbas

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.


2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Elissavet Kentepozidou ◽  
Sarah J. Aitken ◽  
Christine Feig ◽  
Klara Stefflova ◽  
Ximena Ibarra-Soria ◽  
...  

Abstract Background CTCF binding contributes to the establishment of a higher-order genome structure by demarcating the boundaries of large-scale topologically associating domains (TADs). However, despite the importance and conservation of TADs, the role of CTCF binding in their evolution and stability remains elusive. Results We carry out an experimental and computational study that exploits the natural genetic variation across five closely related species to assess how CTCF binding patterns stably fixed by evolution in each species contribute to the establishment and evolutionary dynamics of TAD boundaries. We perform CTCF ChIP-seq in multiple mouse species to create genome-wide binding profiles and associate them with TAD boundaries. Our analyses reveal that CTCF binding is maintained at TAD boundaries by a balance of selective constraints and dynamic evolutionary processes. Regardless of their conservation across species, CTCF binding sites at TAD boundaries are subject to stronger sequence and functional constraints compared to other CTCF sites. TAD boundaries frequently harbor dynamically evolving clusters containing both evolutionarily old and young CTCF sites as a result of the repeated acquisition of new species-specific sites close to conserved ones. The overwhelming majority of clustered CTCF sites colocalize with cohesin and are significantly closer to gene transcription start sites than nonclustered CTCF sites, suggesting that CTCF clusters particularly contribute to cohesin stabilization and transcriptional regulation. Conclusions Dynamic conservation of CTCF site clusters is an apparently important feature of CTCF binding evolution that is critical to the functional stability of a higher-order chromatin structure.


2002 ◽  
Vol 32 (1) ◽  
pp. 301-337 ◽  
Author(s):  
Philippa Pattison ◽  
Garry Robins

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.


2014 ◽  
Vol 62 (17) ◽  
pp. 4573-4586 ◽  
Author(s):  
Chunxiao Jiang ◽  
Yan Chen ◽  
K. J. Ray Liu

2017 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett

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.


2019 ◽  
Vol 30 (12) ◽  
pp. 2050007
Author(s):  
Guanghai Cui ◽  
Zhen Wang ◽  
Ling Dong ◽  
Xiaoli Cao ◽  
Yue Liu ◽  
...  

In social networks, resource sharing behaviors always take place in groups of individuals and rely on voluntary cooperation. In this work, first, a multi-player donor recipient game in which strategies describe individuals’ varying degrees of willingness to share resources is formulated, instead of using the limited binary decisions (e.g. share or not share) in a classical donor-recipient game. Second, the evolutionary dynamics of individual strategies are explored under the influence of two contribution-based resource allocation mechanisms: the total contribution-based allocation mechanism (TCAM) and the direct contribution-based allocation mechanism (DCAM). The results indicate that the network is dominated by the full-cooperation strategy when the cost-to-benefit ratio of resources is not too large and the DCAM is more effective than TCAM. Furthermore, the underlying reason why some strategies with higher sharing willingness can coexist in specific situations, is also explained in detail by leveraging macroscopic and microscopic perspective analysis. Finally, the influences of slandering and whitewashing behaviors conducted by a few malicious individuals on the allocation mechanisms are also studied. Current research will offer new insights into understanding the influence and optimizing the resource allocation policies in social networks.


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