Improving Individual Predictions using Social Networks Assortativity

Social networks are known to be assortative with respect to many attributes, such as age, weight, wealth, level of education, ethnicity and gender. This can be explained by influences and homophilies. Independently of its origin, this assortativity gives us information about each node given its neighbors. Assortativity can thus be used to improve individual predictions in a broad range of situations, when data are missing or inaccurate. This paper presents a general framework based on probabilistic graphical models to exploit social network structures for improving individual predictions of node attributes. Using this framework, we quantify the assortativity range leading to an accuracy gain in several situations. We finally show how specific characteristics of the network can improve performances further. For instance, the gender assortativity in real-world mobile phone data changes significantly according to some communication attributes. In this case, individual predictions with 75% accuracy are improved by up to 3%

Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

Inference of Socioeconomic Status in a Communication Graph

In this work, we examine the socio-economic correlations present among users in a mobile phone network in Mexico. First, we find that the distribution of income for a subset of users –for which we have income information given by a large bank in Mexico– follows closely, but not exactly, the income distribution for the whole population of Mexico. We also show the existence of a strong socio-economic homophily in the mobile phone network, where users linked in the network are more likely to have similar income. The main contribution of this work is that we leverage this homophily in order to propose a methodology, based on Bayesian statistics, to infer the socio-economic status for a large subset of users in the network (for which we have no banking information). With our proposed algorithm, we achieve an accuracy of 0.71 in a two-class classification problem (low and high income) which significantly outperforms a simpler method based on a frequentist approach. Finally, we extend the two-class classification problem to multiple classes by using the Dirichlet distribution.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Temporal Social Coordination Through Social Networks

Temporal communication is mainly associated with the concept of time. The social network derived from temporal environment is constantly changing; a communication link can be connected and disconnected highly frequently. Further with the communication technology such as cell phone, time itself has shifted from an absolute time to a relative time. Mobile communication is closely related with temporal communication, due to its micro coordination property and also the constant establishment of links and breakage of links from time to time. To study the network in the temporal domain, we are constrained by the relative time concept. As communication behaviour is highly dynamic, we expect formation of new ties and breakages of existing ties over time. This is especially different when comparing to social network studies conducted through self report surveys as the network through self report survey remains relatively static for the duration of the survey. In our study, we are interested in how a person would be expanding its network only. Thus we use an accumulated network structure to study the total links a person acquires over time and how such influences the network position