Over the past decade, mobile phones have become prevalent in all parts of the world, across all demographic backgrounds. Mobile phones are used by men and women across a wide age range in both developed and developing countries. Consequently, they have become one of the most important mechanisms for social interaction within a population, making them an increasingly important source of information to understand human demographics and human behaviour.
In this work we combine two sources of information: communication logs from a major mobile operator in a Latin American country, and information on the demographics of a subset of the users population. This allows us to perform an observational study of mobile phone usage, differentiated by age groups categories. This study is interesting in its own right, since it provides knowledge on the structure and demographics of the mobile phone market in the studied country.
We then tackle the problem of inferring the age group for all users in the network. We present here an exclusively graph-based inference method relying solely on the topological structure of the mobile network, together with a topological analysis of the performance of the algorithm. The equations for our algorithm can be described as a diffusion process with two added properties: (i) memory of its initial state, and (ii) the information is propagated as a probability vector for each node attribute (instead of the value of the attribute itself). Our algorithm can successfully infer different age groups within the network population given known values for a subset of nodes (seed nodes). Most interestingly, we show that by carefully analysing the topological relationships between correctly predicted nodes and the seed nodes, we can characterize particular subsets of nodes for which our inference method has significantly higher accuracy.
University of Michigan（密歇根大学）；Lawrence Livermore National Lab（劳伦斯利弗莫尔国家实验室）；RWTH Aachen University（亚琛工业大学）
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i.e., the tendency of linked nodes to have similar attributes. Such assortativity is often induced by homophily, the tendency for nodes of similar properties to connect. Homophily can be common in social networks where systemic factors have forced individuals into communities which share a sensitive attribute. Through synthetic graphs, we study the interplay between locally occurring homoph…
Two complementary mechanisms are thought to shape social groups: homophily between agents and structural balance in connected triads. Here we consider $N$ fully connected agents, where each agent has $G$ underlying attributes, and the similarity between agents in attribute space (i.e., homophily) is used to determine the link weight between them. To incorporate structural balance we use a triad-updating rule where only one attribute of one agent is changed intentionally in each update, but this also leads to accidental changes in link weights and even link polarities. The link weight dynamics …
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures p…
Department of Sociology, Cornell University（康奈尔大学社会学系）；Department of Sociology, Dartmouth College（达特茅斯学院社会学系）；Qatar Computing Research Institute（卡塔尔计算研究所）
In online social networks, it is common to use predictions of node categories to estimate measures of homophily and other relational properties. However, online social network data often lacks basic demographic information about the nodes. Researchers must rely on predicted node attributes to estimate measures of homophily, but little is known about the validity of these measures. We show that estimating homophily in a network can be viewed as a dyadic prediction problem, and that homophily estimates are unbiased when dyad-level residuals sum to zero in the network. Node-level prediction model…
Homophily, ranging from demographics to sentiments, breeds connections in social networks, either offline or online. However, with the prosperous growth of music streaming service, whether homophily exists in online music listening remains unclear. In this study, two online social networks of a same group of active users are established respectively in Netease Music and Weibo. Through presented multiple similarity measures, it is evidently demonstrated that homophily does exist in music listening of both online social networks. The unexpected music similarity in Weibo also implies that knowled…
The observation that individuals tend to be friends with people who are similar to themselves, commonly known as homophily, is a prominent and well-studied feature of social networks. Many machine learning methods exploit homophily to predict attributes of individuals based on the attributes of their friends. Meanwhile, recent work has shown that gender homophily can be weak or nonexistent in practice, making gender prediction particularly challenging. In this work, we identify another useful structural feature for predicting gender, an overdispersion of gender preferences introduced by indivi…
University of Luxembourg（卢森堡大学）；Imperial College London（伦敦帝国理工学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively unexplored. This problem becomes especially relevant in fields where a scarcity of labels exists. Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily. Specif…
Trustworthy Neighborhoods Mining: Homophily-Aware Neutral Contrastive Learning for Graph Clustering
Shantou University（汕头大学）；College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院）；School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University（安格利亚鲁斯金大学科学与工程学部计算与信息科学学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
On social networks, while nodes bear rich attributes, we often lack the `semantics' of why each link is formed-- and thus we are missing the `road signs' to navigate and organize the complex social universe. How to identify relationship semantics without labels? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., education) and, as output, learn a set of social affin…
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, …
People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show analytically that these biases depend on the level of homophily and its asymmetric nature, as well as on the size of the minority group. Our model predictions correspond wel…
Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed due to an individual's demographic attributes such as age group, gender, geographic location, or due to a network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristi…
ISI Foundation（ISI基金会）；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（阿根廷国家科学与技术研究理事会）；PathSight Predictive Science Inc.（PathSight预测科学公司）
Personal electronic devices including smartphones give access to behavioural signals that can be used to learn about the characteristics and preferences of individuals. In this study, we explore the connection between demographic and psychological attributes and the digital behavioural records, for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. Along with the demographic data, we collected self-reported assessments on validated psychometric questionnaires for moral traits and basic human value…
The massive popularity of online social media provides a unique opportunity for researchers to study the linguistic characteristics and patterns of user's interactions. In this paper, we provide an in-depth characterization of language usage across demographic groups in Twitter. In particular, we extract the gender and race of Twitter users located in the U.S. using advanced image processing algorithms from Face++. Then, we investigate how demographic groups (i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and also their interests. We extract linguistic features from …
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase history can be used for multi-task prediction of many demographic fields such as gender and marital status. Here we present an embedding based method to integrate multifaceted sequences of transaction data, together with auxiliary relational tables, for better user modeling and d…
The tendency for individuals to form social ties with others who are similar to themselves, known as homophily, is one of the most robust sociological principles. Since this phenomenon can lead to patterns of interactions that segregate people along different demographic dimensions, it can also lead to inequalities in access to information, resources, and opportunities. As we consider potential interventions that might alleviate the effects of segregation, we face the challenge that homophily constitutes a pervasive and organic force that is difficult to push back against. Designing effective …
In this paper, we investigate whether text from a Community Question Answering (QA) platform can be used to predict and describe real-world attributes. We experiment with predicting a wide range of 62 demographic attributes for neighbourhoods of London. We use the text from QA platform of Yahoo! Answers and compare our results to the ones obtained from Twitter microblogs. Outcomes show that the correlation between the predicted demographic attributes using text from Yahoo! Answers discussions and the observed demographic attributes can reach an average Pearson correlation coefficient of \r{ho}…
Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups. We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning (ML) methods to predict demographic features (namely, age and gender) of unlabeled users by leveraging individual calling patter…
Electronic communication records provide detailed information about temporal aspects of human interaction. Previous studies have shown that individuals' communication patterns have complex temporal structure, and that this structure has system-wide effects. In this paper we use mobile phone records to show that interaction patterns involving multiple individuals have non-trivial temporal structure that cannot be deduced from a network presentation where only interaction frequencies are taken into account. We apply a recently introduced method, temporal motifs, to identify interaction patterns …
Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups, this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erron…
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classi…
We study the structure of the social graph of mobile phone users in the country of Mexico, with a focus on demographic attributes of the users (more specifically the users' age). We examine assortativity patterns in the graph, and observe a strong age homophily in the communications preferences. We propose a graph based algorithm for the prediction of the age of mobile phone users. The algorithm exploits the topology of the mobile phone network, together with a subset of known users ages (seeds), to infer the age of remaining users. We provide the details of the methodology, and show experimen…
The task of node classification is to infer unknown node labels, given the labels for some of the nodes along with the network structure and other node attributes. Typically, approaches for this task assume homophily, whereby neighboring nodes have similar attributes and a node's label can be predicted from the labels of its neighbors or other proximate (i.e., nearby) nodes in the network. However, such an assumption may not always hold -- in fact, there are cases where labels are better predicted from the individual attributes of each node rather than the labels of its proximate nodes. Ideall…
In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Forecasting new edges in online social networks can give us a better understanding of the growth of these networks. There have been many studies of link prediction in the field of engineering and humanities. Scientists attribute the existence of a new relationship between two individuals for two reasons: 1) Proximity to the graph (structure) 2) Similar properties of the two individuals (Homophile law).…
Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 % accuracy), whereas the har…
This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of rich structural properties found in real-world networks. We make three contributions. First, we propose a parsimonious and accurate model of attributed network growth that jointly explains the emergence of in-degree distributions, local clustering, clustering-degree relationship and attribute mixing patterns. Second, our model is based on bias…
The effects of social influence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. Recently, Yin et al. proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with restart algorithm to the SAN framework and showed improved performance. In this paper, we extend the SAN framework with several leading supervised and unsu…
Heterogeneity is a key aspect of complex networks, often emerging by looking at the distribution of node properties, from the milestone observations on the degree to the recent developments in mixing pattern estimation. Mixing patterns, in particular, refer to nodes' connectivity preferences with respect to an attribute label. Social networks are mostly characterized by assortative/homophilic behaviour, where nodes are more likely to be connected with similar ones. Recently, assortative mixing is increasingly measured in a multi-scale fashion to overcome well known limitations of classic score…
Mexico's National Institute of Geriatrics (INGER) is the national research center of reference for matters related to human aging. INGER scientists perform basic, clinical and demographic research which may imply different scientific cultures working together in the same specialized institution. In this paper, by a combination of text mining, co-authorship network analysis and agent-based modeling we analyzed and modeled the team assembly practices and the structure of the knowledge produced by scientists from INGER. Our results showed a weak connection between basic and clinical research, and…
Censuses around the world are key sources of data to guide government investments and public policies. However, these sources are very expensive to obtain and are collected relatively infrequently. Over the last decade, there has been growing interest in the use of data from social media to complement traditional data sources. However, social media users are not representative of the general population. Thus, analyses based on social media data require statistical adjustments, like post-stratification, in order to remove the bias and make solid statistical claims. These adjustments are possibl…
