Xutong Liu, Y. Chen, John C.S. Lui, Konstantin Avrachenkov
Abstract Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.
{"title":"Learning to count: A deep learning framework for graphlet count estimation","authors":"Xutong Liu, Y. Chen, John C.S. Lui, Konstantin Avrachenkov","doi":"10.1017/nws.2020.35","DOIUrl":"https://doi.org/10.1017/nws.2020.35","url":null,"abstract":"Abstract Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43542694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Personalized PageRank has found many uses in not only the ranking of webpages, but also algorithmic design, due to its ability to capture certain geometric properties of networks. In this paper, we study the diffusion of PageRank: how varying the jumping (or teleportation) constant affects PageRank values. To this end, we prove a gradient estimate for PageRank, akin to the Li–Yau inequality for positive solutions to the heat equation (for manifolds, with later versions adapted to graphs).
{"title":"Gradient and Harnack-type estimates for PageRank","authors":"P. Horn, Lauren M. Nelsen","doi":"10.1017/nws.2020.34","DOIUrl":"https://doi.org/10.1017/nws.2020.34","url":null,"abstract":"Abstract Personalized PageRank has found many uses in not only the ranking of webpages, but also algorithmic design, due to its ability to capture certain geometric properties of networks. In this paper, we study the diffusion of PageRank: how varying the jumping (or teleportation) constant affects PageRank values. To this end, we prove a gradient estimate for PageRank, akin to the Li–Yau inequality for positive solutions to the heat equation (for manifolds, with later versions adapted to graphs).","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57043443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-01Epub Date: 2020-03-09DOI: 10.1017/nws.2020.2
Won-Tak Joo, Jason Fletcher
Who is more likely to be isolated from society in terms of political beliefs? To answer this question, we measure whether individuals' beliefs are "out of sync" - the extent to which their views differ with their contemporaries - and examine how the level of synchronization is associated with the size of important-matter and political-matter discussion networks. The results show that people with weaker belief synchronization are more likely to have smaller important-matter discussion networks. However, additional analyses of political-matter discussion networks show that weaker belief synchronization is associated with smaller networks only among those without a high school diploma and even provides some advantage in maintaining larger networks for the college-educated. Overall, the results imply that political beliefs that are "out of sync" correspond to the individual being "out of society," whereas the aspects of "out of society" are quite different among educational groups.
{"title":"Out of Sync, Out of Society: Political Beliefs and Social Networks.","authors":"Won-Tak Joo, Jason Fletcher","doi":"10.1017/nws.2020.2","DOIUrl":"10.1017/nws.2020.2","url":null,"abstract":"<p><p>Who is more likely to be isolated from society in terms of political beliefs? To answer this question, we measure whether individuals' beliefs are \"out of sync\" - the extent to which their views differ with their contemporaries - and examine how the level of synchronization is associated with the size of important-matter and political-matter discussion networks. The results show that people with weaker belief synchronization are more likely to have smaller important-matter discussion networks. However, additional analyses of political-matter discussion networks show that weaker belief synchronization is associated with smaller networks only among those without a high school diploma and even provides some advantage in maintaining larger networks for the college-educated. Overall, the results imply that political beliefs that are \"out of sync\" correspond to the individual being \"out of society,\" whereas the aspects of \"out of society\" are quite different among educational groups.</p>","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646439/pdf/nihms-1549804.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38579489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper investigates the processes involved when newly hired employees need to simultaneously build up and mobilize personal network ties during their organizational socialization. It focuses on the quality of ties at an early formative stage, characterized by the lack of a tie history between actors. Social capital theory would suggest that such nascent ties do not offer optimal channels for the kind and volume of resources that newcomers (need to) rely on during socialization. To better understand how this apparent mismatch between tie quality and resource needs is handled from an ego-centered perspective, the paper analyzes personal network data from 24 newcomers in nine organizations, using an adapted form of Qualitative Structural Analysis. Three tie-level qualities are found to explain how the lack of tie history may be alleviated, circumvented, or compensated. They comprise (a) variants of openness experienced with stronger ties, (b) perceptions of a lowered threshold towards weaker ties, and (c) sources of legitimacy regarding latent ties. Based on these findings, the paper presents an integrated conceptual model to clarify how nascent ties offer channels for network resources during socialization and discusses the need for further research on the role of specific moderators for the investigated processes.
{"title":"Mobilizing nascent ties: A Qualitative Structural Analysis of social(izing) capital in newcomer networks","authors":"Sabine R. Bakker","doi":"10.1017/nws.2020.25","DOIUrl":"https://doi.org/10.1017/nws.2020.25","url":null,"abstract":"Abstract This paper investigates the processes involved when newly hired employees need to simultaneously build up and mobilize personal network ties during their organizational socialization. It focuses on the quality of ties at an early formative stage, characterized by the lack of a tie history between actors. Social capital theory would suggest that such nascent ties do not offer optimal channels for the kind and volume of resources that newcomers (need to) rely on during socialization. To better understand how this apparent mismatch between tie quality and resource needs is handled from an ego-centered perspective, the paper analyzes personal network data from 24 newcomers in nine organizations, using an adapted form of Qualitative Structural Analysis. Three tie-level qualities are found to explain how the lack of tie history may be alleviated, circumvented, or compensated. They comprise (a) variants of openness experienced with stronger ties, (b) perceptions of a lowered threshold towards weaker ties, and (c) sources of legitimacy regarding latent ties. Based on these findings, the paper presents an integrated conceptual model to clarify how nascent ties offer channels for network resources during socialization and discusses the need for further research on the role of specific moderators for the investigated processes.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48483077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Wellman, Anabel Quan-Haase, Molly-Gloria Harper
Abstract We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.
{"title":"The networked question in the digital era: How do networked, bounded, and limited individuals connect at different stages in the life course?","authors":"B. Wellman, Anabel Quan-Haase, Molly-Gloria Harper","doi":"10.1017/nws.2019.28","DOIUrl":"https://doi.org/10.1017/nws.2019.28","url":null,"abstract":"Abstract We used in-depth interviews with 101 participants in the East York section of Toronto, Canada to understand how digital media affects social connectivity in general—and networked individualism in particular—for people at different stages of the life course. Although people of all ages intertwined their use of digital media with their face-to-face interactions, younger adults used more types of digital media and have more diversified personal networks. People in different age-groups conserved media, tending to stick with the digital media they learned to use in earlier life stages. Approximately one-third of the participants were Networked Individuals: In each age-group, they were the most actively using digital media to maintain ties and to develop new ones. Another one-third were Socially Bounded, who often actively used digital media but kept their connectivity within a smaller set of social groups. The remaining one-third, who were Socially Limited, were the least likely to use digital media. Younger adults were the most likely to be Networked Individuals, leading us to wonder if the percentage of the population who are Bounded or Limited will decline over time.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2019.28","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43499321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study documents historical trends of size and political diversity in Americans’ discussion networks, which are often seen as important barometers of social and political health. Contrasting findings from data drawn out of a nationally representative survey experiment of 1,055 Americans during the contentious 2016 U.S. presidential election to data arising from 11 national data sets covering nearly three decades, we find that Americans’ core networks are significantly smaller and more politically homogeneous than at any other period. Several methodological artifacts seem unlikely to account for the effect. We show that in this period, more than before, “important matters” were often framed as political matters, and that this association probably accounts for the smaller networks.
{"title":"Political isolation in America","authors":"Byungkyu Lee, P. Bearman","doi":"10.1017/nws.2020.9","DOIUrl":"https://doi.org/10.1017/nws.2020.9","url":null,"abstract":"Abstract This study documents historical trends of size and political diversity in Americans’ discussion networks, which are often seen as important barometers of social and political health. Contrasting findings from data drawn out of a nationally representative survey experiment of 1,055 Americans during the contentious 2016 U.S. presidential election to data arising from 11 national data sets covering nearly three decades, we find that Americans’ core networks are significantly smaller and more politically homogeneous than at any other period. Several methodological artifacts seem unlikely to account for the effect. We show that in this period, more than before, “important matters” were often framed as political matters, and that this association probably accounts for the smaller networks.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47092842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract A person’s egonet, the set of others with whom that person is connected, is a personal sample of society which especially influences that person’s experience and perceptions of society. We show that egonets systematically misrepresent the general population because each person is included in as many egonets as that person has “friends.” Previous research has recognized that this unequal weighting in egonets leads many people to find that their friends have more friends than they themselves have. This paper builds upon that research to show that people’s egonets provide them with systematically biased samples of the population more generally. We discuss how this ubiquitous egonet bias may have far reaching implications for people’s experiences and perceptions of frequencies of other people’s ties and traits in ways that may influence their own feelings and behaviors. In particular, these egonet biases may help explain people’s tendencies to disproportionately experience and overestimate the prevalence of certain types of deviance and other social behaviors and consequently be influenced toward them. We illustrate egonet bias with analyses of all friends among 63,731 Facebook users. We call for further empirical investigation of egonet biases and their consequences for individuals and society.
{"title":"Egonets as systematically biased windows on society","authors":"S. Feld, Alec McGail","doi":"10.1017/nws.2020.5","DOIUrl":"https://doi.org/10.1017/nws.2020.5","url":null,"abstract":"Abstract A person’s egonet, the set of others with whom that person is connected, is a personal sample of society which especially influences that person’s experience and perceptions of society. We show that egonets systematically misrepresent the general population because each person is included in as many egonets as that person has “friends.” Previous research has recognized that this unequal weighting in egonets leads many people to find that their friends have more friends than they themselves have. This paper builds upon that research to show that people’s egonets provide them with systematically biased samples of the population more generally. We discuss how this ubiquitous egonet bias may have far reaching implications for people’s experiences and perceptions of frequencies of other people’s ties and traits in ways that may influence their own feelings and behaviors. In particular, these egonet biases may help explain people’s tendencies to disproportionately experience and overestimate the prevalence of certain types of deviance and other social behaviors and consequently be influenced toward them. We illustrate egonet bias with analyses of all friends among 63,731 Facebook users. We call for further empirical investigation of egonet biases and their consequences for individuals and society.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44734968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew E. Brashears, Laura Aufderheide Brashears, Nicolas L. Harder
Abstract Ego networks are thought to be influenced by the opportunities provided to associate with others given by our master statuses (e.g., race or sex), by the preferences individuals possess for interaction given our personality traits (e.g., extroverted or neurotic), and by the capacity to manage interactions on an ongoing basis given our cognitive ability to recall network information. However, prior research has been unable to examine all three classes of predictors concurrently. We rectify this deficiency in the literature by using a novel dataset of nearly 1000 respondents collected using controlled laboratory designs; using this dataset, we can simultaneously examine the impact of master statuses, personality traits, and social cognitive competencies on ego network size, structure (i.e., density), and composition (i.e., diversity). We find that all classes of predictors influence our ego networks, though in different ways, and point to new avenues for research into human sociability.
{"title":"Where you are, what you want, and what you can do: The role of master statuses, personality traits, and social cognition in shaping ego network size, structure, and composition","authors":"Matthew E. Brashears, Laura Aufderheide Brashears, Nicolas L. Harder","doi":"10.1017/nws.2020.6","DOIUrl":"https://doi.org/10.1017/nws.2020.6","url":null,"abstract":"Abstract Ego networks are thought to be influenced by the opportunities provided to associate with others given by our master statuses (e.g., race or sex), by the preferences individuals possess for interaction given our personality traits (e.g., extroverted or neurotic), and by the capacity to manage interactions on an ongoing basis given our cognitive ability to recall network information. However, prior research has been unable to examine all three classes of predictors concurrently. We rectify this deficiency in the literature by using a novel dataset of nearly 1000 respondents collected using controlled laboratory designs; using this dataset, we can simultaneously examine the impact of master statuses, personality traits, and social cognitive competencies on ego network size, structure (i.e., density), and composition (i.e., diversity). We find that all classes of predictors influence our ego networks, though in different ways, and point to new avenues for research into human sociability.","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/nws.2020.6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46255077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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