Measure of Node Similarity in Multilayer Networks

Scientific Reports, 2019

Social network sites (SNS) have touched the lives of millions of people around the world. People share interests, ideas, photos, activities in the social networks with their family, colleagues, friends and acquaintances. However, the degree of interactions among members widely varies. According to a sociology principle, people with similar personality often interact with each other more frequently. A group of connected people with similar personality traits is termed as a homophily. In this paper, we develop a method to identify homophilies by analyzing the Big5 personality traits of users from their interactions in an egocentric network like Facebook. We observe that our homophilies correctly cluster ranged from 73 to 87 % users for different personality traits. We also present a novel validation technique to verify those extracted homophilies in real life. Note that we are the first to validate the extracted homophilies and compare those with baseline techniques from SNS usage in real life using an interview-based method. We notice that our validation results show different agreements ranged from 0.207 (fair) to 0.709 (substantial) among the raters of those homo-philies in real-life .

"The analysis of social networks has boomed recently, mainly as online social networking systems such as Twitter allow researchers to access these data. However, the research is less and less focused on the fundamental question on the validity of the data and interpretation of the results. For example, Golder et al. (2007) use the word 'friend' in quotes while describing their results. To enhance the discussion around the validity of results, our work contributes a categorization of social network data. We also discuss the differences of the data sources, especially highlighting the fact that different data sources disclose different kinds of networks. Our approach is to examine social networks based on several sources of data, and thus acquire a richer data set. Based on this extended data set, we are more equipped to understand the social relations represented via links between nodes. After reviewing the existing literature, we make two observations of social relationships in online services. Firstly, the friendship data may be shared in public or with the specific group of users of that service - this may affect how people perceive and use these relationships, especially when compared with the private displays of relations (e.g., Donath & boyd, 2004). On the other hand, people interact only with part of their social relations (e.g., Golder et al., 2007) and research has started to focus from statical networks to more dynamical activity based networks (e.g., Huberman et al., 2009). Based on the existing literature, shortly discussed above, a 2x2 matrix can be developed. Relations may be public or private and active or passive. For instance, those relations with which you use Instant Messaging can be considered private and active whereas Facebook friends are passive and public. As they are different in this nature, also the conclusions based on the analysis should differ. After confirming that the data measure the phenomenon desired, one should use several kinds of data sources to really understand the social structures behind the group under study. We claim that multiple data sets should be used when measuring social relations. McPherson et al. (2001) have also concluded that the priority for future social network researchers should be to gather dynamic data on multiple social relations. By studying existing research and our own empirical data (e.g., Karikoski & Nelimarkka, 2011), we discuss the opportunities and challenges of using multiple data sets to cover the same group. Abstract references Donath, J. and Boyd, D., 2004. Public displays of connection. BT Technology Journal, 22 (4), pp.71-82. Golder, S.A., Wilkinson, D. and Huberman, B.A., 2007. Rhythms of social interaction: messaging within a massive online network. In: The 3rd International Conference on Communities and Technologies (C&T2007). East Lansing, Michigan, USA 28-30 June 2007. Huberman, B., Romero, D.M. and Wu, F., 2009. Social networks that matter: Twitter under microscope. First Monday, 14 (1-5). Karikoski, J. and Nelimarkka, M., 2011. Measuring social relations with multiple data sets. International Journal of Social Computing and Cyber-Physical Systems (IJSCCPS), 1 (1), pp.98-113. McPherson, M., Smith-Lovin, L. and Cook, J.M., 2001. Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27, pp.415-444."

di.unito.it, 2012

Social media have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here we study the presence of homophily in three systems that combine tagging of social media with online social networks. We find a substantial level of topical similarity among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92%.

Social Science Computer Review, 2020

The similarity between pairs of people is often measured on relatively static traits and at a given point in time. Moving beyond this approach, a burgeoning line of research is investigating temporal dyadic similarity on traits and behaviors, such as health activities. Our study contributes to this line of inquiry by using fine-grained longitudinal data obtained from sensors, mobile devices, and surveys to examine whether we can observe distinct types of dyadic similarity trajectories based on physical activity, and if so, what dyad-level properties predict membership in each trajectory class. Treating daily differences in the steps for dyads as time series, we use k-shape clustering to identify classes of similarity trajectories and logistic regression to examine the link between trajectory class and key dyad-level factors. We identify 21 dyadic trajectory clusters and find that trajectory membership predicts dyadic connectivity in the communication network, as well as racial and r...

Current Zoology, 2021

The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the part...

Social Network Analysis and Mining, 2015

Many interconnected systems and particularly social interactions can be modeled as networks. These networks often exhibit common properties such as high clustering coefficient, low average path lengths and degree distributions following power-law. Networks having these properties are called small world-scale free networks or simply complex networks. Recent interest in complex networks has catalysed the development of algorithmic models to artificially generate these networks.

ArXiv, 2021

In networks, the well-documented tendency for people with similar characteristics to form connections is known as the principle of homophily. Being able to quantify homophily into a number has a signicant real-world impact, ranging from government fund-allocation to netuning the parameters in a sociological model. This paper introduces the “Popularity-Homophily Index” (PH Index) as a new metric to measure homophily in directed graphs. The PH Index takes into account the “popularity” of each actor in the interaction network, with popularity precalculated with an importance algorithm like Google PageRank. The PH Index improves upon other existing measures by weighting a homophilic edge leading to an important actor over a homophilic edge leading to a less important actor. The PH Index can be calculated not only for a single node in a directed graph but also for the entire graph. This paper calculates the PH Index of two sample graphs, and concludes with an overview of the strengths ...

2013

The availability of advanced social interaction sensing technologies provides fine grained data for social network analysis. Although traditional methods of gathering social network data may be subject to human ability to recall social details, people's rating of their closeness to persons in their network is important.

Complexity

We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We th...