The Spatial Dimensions of Social Networks

2020 ◽  
pp. 367-383
Author(s):  
Zachary P. Neal
Keyword(s):  
Social Networks ◽  
Topological Space ◽  
Network Science ◽  
Spatial Dimensions

The first law of geography holds that everything is related to everything else, but near things are more related than distant things, where distance refers to topographical space. If a first law of network science exists, it would similarly hold that everything is related to everything else, but near things are more related than distant things, but where distance refers to topological space. Frequently these two laws collide, together holding that everything is related to everything else, but topographically and topologically near things are more related than topographically and topologically distant things. The focus of the spatial study of social networks lies in exploring a series of questions embedded in this combined law of geography and networks. This chapter explores the questions that have been asked and the answers that have been offered at the intersection of geography and networks.

scholarly journals How to Improve Network Science: the Potential of (Empirically Calibrated and Validated) Agent-Based Modelling

2021 ◽  
Author(s):  
Edmund Chattoe-Brown ◽  
Simone GABBRIELLINI
Keyword(s):  
Social Networks ◽  
Network Science ◽  
Social Processes ◽  
Scientific Contribution ◽  
Agent Based ◽  
Calibration And Validation ◽  
Agent Based Modelling ◽  
Empirical Calibration ◽  
Individual Cognition ◽  
Effective Contribution

This article argues that the potential of Agent-Based Modelling (the capability for empirical justification of computer programmes representing social processes as dynamically unfolding individual cognition, action and interaction to reveal emerging aggregate outcomes) is not yet fully realised in the scientific study of social networks. By critically analysing several existing studies, it shows why the technique’s distinctive methodology (involving empirical calibration and validation) is just as important to its scientific contribution as its novel technical capabilities. The article shows the advantages of Agent-Based Models following this methodology and distinguishes these clearly from the implications of apparently similar techniques (like actor-based approaches). The article also discusses the limitations of existing Agent-Based Modelling applied to social networks, enabling the approach to make a more effective contribution to Network Science in future.

scholarly journals Measuring educational segregation using a whole population network of the Netherlands

2021 ◽  
Author(s):  
Jan van der Laan ◽  
Marjolijn Das ◽  
Saskia te Riele ◽  
Edwin de Jonge ◽  
Tom Emery
Keyword(s):  
Social Sciences ◽  
Social Networks ◽  
Random Walk ◽  
The Netherlands ◽  
Network Science ◽  
Extended Family ◽  
Educational Institution ◽  
Educational Backgrounds ◽  
The Social ◽  
New Applications

In this analysis we present a whole population network which uses administrative data to construct a network incorporating 1.4 billion relationships between the 17 million inhabitants of the Netherlands. Relationships are identified between individuals who live in the same household, live close to each other, work for the same company, attend the same educational institution, or belong to the same extended family. This network has properties that are rare in observed social networks, which opens up new applications for network science in the social sciences. To demonstrate the applications of such a network, we use a random walk approach to estimate segregation of individuals from differing educational backgrounds and whether specific types of relationships increase or decrease this segregation. The results suggest that relationships between people in the same household greatly increase segregation whilst work, school and neighborhood networks relationships increase exposure to individuals with different backgrounds. The size of these effects is context dependent. Further applications of a whole population network are also discussed

scholarly journals Modeling Network Dynamics

2017 ◽  
Author(s):  
Christopher Steven Marcum ◽  
David R. Schaefer
Keyword(s):  
Stochastic Process ◽  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Network Science ◽  
Dynamic Network ◽  
Process Theory ◽  
Network Data ◽  
Network Change ◽  
Human Actors

One of the great lessons from the last half century of research on social networks is that relationships are constantly in flux. While much social network analysis focuses on static relationships between actors, there is also a rich tradition of work extending back to foundational studies in network science focused on the notion that network change is an indelible aspect of social life for human and non-human actors alike (e.g., Bott, 1957; Heider, 1946; Newcomb 1961; Rapoport, 1949; Sampson, 1969). Today, social network researchers benefit from this history in that a host of methods to collect and analyze such dynamic network data have been developed. Among them, the methods based on stochastic process theory have given rise to a paradigm where inferences and predictions can be made on the mechanisms that drive changes in social structure.

scholarly journals Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

2021 ◽  
Vol 4 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Theory ◽  
Network Science ◽  
Prediction Models ◽  
Traditional Approach ◽  
Computational Social Science ◽  
Data Driven ◽  
Social Data ◽  
Causal Explanations

The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).

scholarly journals Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networks

2019 ◽  
Vol 7 (4) ◽  
pp. 476-497 ◽  
Author(s):  
Raphaël Charbey ◽  
Christophe Prieur
Keyword(s):  
Social Networks ◽  
Network Science ◽  
Structural Characteristics ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Random Networks ◽  
Ego Networks ◽  
Limited Size ◽  
Structural Differences

AbstractNetwork science gathers methods coming from various disciplines which sometimes hardly cross the boundaries between these disciplines. Widely used in molecular biology in the study of protein interaction networks, the enumeration, in a network, of all possible subgraphs of a limited size (usually around four or five nodes), often called graphlets, can only be found in a few works dealing with social networks. In the present work, we apply this approach to an original corpus of about 10,000 non-overlapping Facebook ego networks gathered from voluntary participants by a survey application. To deal with so many similar networks, we adapt the relative graphlet frequency to a measure that we call graphlet representativity, which we show to be more effective to classify random networks having slight structural differences. From our data, we produce two clusterings, one of graphlets (paths, star-like, holes, light triangles, and dense), one of networks. The latter is presented with a visualization scheme using our representativity measure. We describe the distinct structural characteristics of the five clusters of Facebook ego networks so obtained and discuss the empirical differences between results obtained with 4-node and 5-node graphlets. We also provide suggestions of follow-ups of this work, both in sociology and in network science.

scholarly journals Network Position and Police Who Shoot

2020 ◽  
Vol 687 (1) ◽  
pp. 89-112 ◽  
Author(s):  
Linda Zhao ◽  
Andrew V. Papachristos
Keyword(s):  
Social Networks ◽  
Police Officers ◽  
Statistical Models ◽  
Network Science ◽  
Police Department ◽  
Police Misconduct ◽  
Chicago Police Department ◽  
Network Position ◽  
The Social ◽  
Chicago Police

This study applies the growing field of network science to explore whether police violence is associated with characteristics of an officer’s social networks and his or her placement within those networks. To do this, we re-create the network of police misconduct for the Chicago Police Department using more than 38,442 complaints filed against police officers between 2000 and 2003. Our statistical models reveal that officers who shoot at civilians are often “brokers” within the social networks of policing, occupying important positions between other actors in the network and often connecting otherwise disconnected parts of the social structure between other officers within larger networks of misconduct. This finding holds, even net measures of officer activity, career movement, and sociodemographic background. Our finding suggest that policies and interventions aimed at curbing police shootings should include not only individual assessments of risk but also an understanding of officers’ positions within larger social networks.

scholarly journals COVID-19 and Networks

2021 ◽  
Author(s):  
Tsuyoshi Murata
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Network Science ◽  
Influence Maximization ◽  
Disease Spread ◽  
Temporal Networks ◽  
Epidemic Modeling ◽  
Influential Individuals

AbstractOngoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Social Networks and Cognition

2020 ◽  
Vol 46 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Edward Bishop Smith ◽  
Raina A. Brands ◽  
Matthew E. Brashears ◽  
Adam M. Kleinbaum
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Science ◽  
Social Information ◽  
Modern Conception ◽  
Modern View ◽  
The Brain

Social network analysis, now often thought of simply as network science, has penetrated nearly every scientific and many scholarly fields and has become an indispensable resource. Yet, social networks are special by virtue of being specifically social, and our growing understanding of the brain is affecting our understanding of how social networks form, mature, and are exploited by their members. We discuss the expanding research on how the brain manages social information, how this information is heuristically processed, and how network cognitions are affected by situation and circumstance. In the process, we argue that the cognitive turn in social networks exemplifies the modern conception of the brain as fundamentally reprogrammable by experience and circumstance. Far from social networks being dependent upon the brain, we anticipate a modern view in which cognition and social networks coconstitute each other.

scholarly journals Unified Representation and Lifted Sampling for Generative Models of Social Networks

2017 ◽  
Author(s):  
Pablo Robles-Granda ◽  
Sebastian Moreno ◽  
Jennifer Neville
Keyword(s):  
Social Networks ◽  
Statistical Models ◽  
Sampling Method ◽  
Network Science ◽  
Network Models ◽  
Generative Models ◽  
Latent Structure ◽  
Statistical Sampling ◽  
Sampling Algorithms ◽  
Context Specific

Statistical models of network structure are widely used in network science to reason about the properties of complex systems—where the nodes and edges represent entities and their relationships. Recently, a number of generative network models (GNM) have been developed that accurately capture characteristics of real world networks, but since they are typically defined in a procedural manner, it is difficult to identify commonalities in their structure. Moreover, procedural definitions make it difficult to develop statistical sampling algorithms that are both efficient and correct. In this paper, we identify a family of GNMs that share a common latent structure and create a Bayesian network (BN) representation that captures their common form. We show how to reduce two existing GNMs to this representation. Then, using the BN representation we develop a generalized, efficient, and provably correct, sampling method that exploits parametric symmetries and deterministic context-specific dependence. Finally, we use the new representation to design a novel GNM and evaluate it empirically.

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