Pedro O. S. Vaz de Melo

Surprising patterns for the call duration distribution of mobile phone users

How long are the phone calls of mobile users? What are the chances of a call to end, given its current duration? Here we answer these questions by studying the call duration distributions (CDDs) of individual users in large mobile networks. We analyzed a large, real network of 3.1 million users and more than one billion phone call records from a private mobile phone company of a large city, spanning 0.1TB. Our first contribution is the TLAC distribution to fit the CDD of each user; TLAC is the truncated version of so-called log-logistic distribution, a skewed, power-law-like distribution. We show that the TLAC is an excellent fit for the overwhelming majority of our users (more than 96% of them), much better than exponential or lognormal. Our second contribution is the MetaDist to model the collective behavior of the users given their CDDs. We show that the MetaDist distribution accurately and succinctly describes the calls duration behavior of users in large mobile networks. All of our methods are fast, and scale linearly with the number of customers.

A Picture of Instagram is Worth More Than a Thousand Words: Workload Characterization and Application

Participatory sensing systems (PSSs) have the potential to become fundamental tools to support the study, in large scale, of urban social behavior and city dynamics. To that end, this work characterizes the photo sharing system Instagram, considered one of the currently most popular PSS on the Internet. Based on a dataset of approximately 2.3 million shared photos, we characterize users behavior in the system showing that there are several advantages and opportunities for large scale sensing, such as a global coverage at low cost, but also challenges, such as a very unequal photo sharing frequency, both spatially and temporally. We also observe that the temporal photo sharing pattern is a good indicator about cultural behaviors, and also says a lot about certain classes of places. Moreover, we present an application to identify regions of interest in a city based on data obtained from Instagram, which illustrates the promising potential of PSSs for the study of city dynamics.

Large-scale study of city dynamics and urban social behavior using participatory sensing

The ubiquitous availability of computing technology such as smartphones, tablets, and other easily portable devices, and the worldwide adoption of social networking sites make it increasingly possible for one to be connected and continuously contribute to this massively distributed information publishing process. In this scenario, people act as social sensors, voluntarily providing data that capture their daily life experiences, and offering diverse observations on both the physical world (e.g., location) and the online world (e.g., events). This large amount of social data can provide new forms of valuable information that are currently not available on this scale via any traditional data collection methods, and can be used to enhance decision making processes. In this article, we argue that location-based social media systems, such as Instagram and Foursquare, can act as valuable sources of large-scale sensing, providing access to important characteristics of urban social behavior much more quickly than traditional methods. We also discuss different applications and techniques that can exploit the data shared in these systems to enable large-scale and near-real-time analyses and visualization of different aspects of city dynamics.

Visualizing the Invisible Image of Cities

With the recent advances in technology, we have the opportunity to study the city dynamics at a large scale. A fundamental step is to be able to sense extensive areas. Participatory sensor networks have the potential to become a very fundamental tool to study social behavior at a large scale. Currently, there are some location sharing services, such as Foursquare, that record the users location along the time, which is clearly one of dimensions in the city dynamics. In this work, we propose a technique called city image and show its applicability taking as examples eight different cities. The resulting image is a way of summarizing the city dynamics based on transition graphs, which map the movements of individuals in a PSN. This technique is a promising way to better understand the city dynamics, helping us to visualize the invisible images of cities.

Quantifying reciprocity in large weighted communication networks

If a friend called you 50 times last month, how many times did you call him back? Does the answer change if we ask about SMS, or e-mails? We want to quantify reciprocity between individuals in weighted networks, and we want to discover whether it depends on their topological features (like degree, or number of common neighbors). Here we answer these questions, by studying the call- and SMS records of millions of mobile phone users from a large city, with more than 0.5 billion phone calls and 60 million SMSs, exchanged over a period of six months. Our main contributions are: (1) We propose a novel distribution, the Triple Power Law (3PL), that fits the reciprocity behavior of all 3 datasets we study, with a better fit than older competitors, (2) 3PL is parsimonious; it has only three parameters and thus avoids over-fitting, (3) 3PL can spot anomalies, and we report the most surprising ones, in our real networks, (4) We observe that the degree of reciprocity between users is correlated with their local topological features; reciprocity is higher among mutual users with larger local network overlap and greater degree similarity.

Traffic Condition Is More Than Colored Lines on a Map: Characterization of Waze Alerts

Participatory sensor network (PSN) enables the understanding of city dynamics and the urban behavioral patterns of their inhabitants. In this work, we focus our analysis on a specific PSN, derived from Waze, for sensing traffic conditions. Our objective is to characterize the properties of this PSN, its broad and global spatial coverage as well as its limitations. We also bring discussions on different opportunities for application design using this network. We claim that the PSN derived from Waze has the potential to help us in the better understanding of traffic problem reasons. Besides that, it could be useful for improving algorithms used in navigation services: (1) by exploiting the provided real-time traffic information or (2) by helping in the identification of valuable pieces of information that are hard to detect with traditional sensors, such as car accidents and potholes.

Challenges and opportunities on the large scale study of city dynamics using participatory sensing

Cities are not identical and evolve over time, and sensing in large scale can be used to capture these differences. Research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in restricted scenarios (e.g., factory). However, sensing large scale areas, such as big cities, brings many challenges and incurs high costs related to system building and management. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices, and building what has been named participatory sensing systems. This work analyzes an emerging type of network derived from this type of system, the Participatory Sensor Network (PSN), where nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on four datasets of participatory sensing systems (27 million of records), we show that this type of network brings many challenges related to structural problems, e.g. instant coverage very limited, and also because of big data issues, which may restrict the use of this emerging type of network. However, it presents also, as shown here, many advantages and open opportunities, mainly related to large scale study of cities dynamics.

Can complex network metrics predict the behavior of NBA teams

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.

RECAST: Telling apart social and random relationships in dynamic networks

Abstract When constructing a social network from interactions among people (e.g., phone calls, encounters), a crucial task is to define the threshold that separates social from random (or casual) relationships. The ability to accurately identify social relationships becomes essential to applications that rely on a precise description of human routines, such as recommendation systems, forwarding strategies and opportunistic dissemination protocols. We thus propose a strategy to analyze users’ interactions in dynamic networks where entities act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST) , allows classifying users interactions, separating random ties from social ones. To that end, RECAST observes how the real system differs from an equivalent one where entities’ decisions are completely random. We evaluate the effectiveness of the RECAST classification on five real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users’ wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing. We show that, for such specific purpose, the relationships inferred by RECAST are more relevant than, e.g., self-declared friendships on Facebook.

Uncovering properties in participatory sensor networks

A fundamental step to achieve the Ubiquitous Computing vision is to sense the environment. The research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in limited size areas, such as forests or volcanoes. However, sensing large scale areas, such as large metropolises, countries, or even the entire planet, brings many challenges. For instance, consider the high cost associated with building and managing such large scale systems. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices (e.g., sensor-enabled cell phones). Systems that enable the user participation with sensed data are named participatory sensing systems. This work analyzes a new type of network derived from this type of system. In this network, nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on two datasets of participatory sensing systems, we show that this type of network has many advantages and fascinating opportunities, such as planetary scale sensing at small cost, but also has many challenges, such as the highly skewed spatial-temporal sensing frequency.