C. A. Piña-García

Spiraling Facebook: an alternative Metropolis–Hastings random walk using a spiral proposal distribution

Sampling the content of an Online Social Network (OSN) is a major application area due to the growing interest in collecting social information e.g., email, location, age and number of friends. Large-scale social networks such as Facebook can be difficult to sample due to the amount of data and the privacy settings imposed by this company. Sampling techniques require the development of reliable algorithms able to cope with an unknown environment. Our main purpose in this manuscript is to examine whether it is possible to switch the normal distribution of the Metropolis–Hasting random walk (MHRW) by using a spiral approach as an alternative and reliable distribution. We propose a sampling algorithm, the Alternative Metropolis–Hasting random walk AMHRW, to study the effect of collecting digital profiles on two different datasets. We examine the soundness and robustness of the proposed algorithm through independent walks on two different representative samples of Facebook. We observe that normal distribution performance can be approximated by means of the use of an Illusion spiral. Similarly, we provide a formal convergence analysis to evaluate the performance of our independent walks and to evaluate whether the sample of draws has attained an equilibrium state. Finally, our preliminary results provide experimental evidence that collecting data with the AMHRW algorithm can be equally effective as the MHRW algorithm on large-scale networks.

Towards a standard sampling methodology on online social networks: collecting global trends on Twitter

One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method aimed to collect and summarize data. Sampling the content of an Online Social Network (OSN) plays an important role as a knowledge discovery tool.It is becoming increasingly difficult to ignore the fact that current sampling methods must cope with a lack of a full sampling frame i.e., there is an imposed condition determined by a limited data access. In addition, another key aspect to take into account is the huge amount of data generated by users of social networking services such as Twitter, which is perhaps the most influential microblogging service producing approximately 500 million tweets per day. In this context, due to the size of Twitter, which is problematic to be measured, the analysis of the entire network is infeasible and sampling is unavoidable.In addition, we strongly believe that there is a clear need to develop a new methodology to collect information on social networks (social mining). In this regard, we think that this paper introduces a set of random strategies that could be considered as a reliable alternative to gather global trends on Twitter. It is important to note that this research pretends to show some initial ideas in how convenient are random walks to extract information or global trends.The main purpose of this study, is to propose a suitable methodology to carry out an efficient collecting process via three random strategies: Brownian, Illusion and Reservoir. These random strategies will be applied through a Metropolis-Hastings Random Walk (MHRW). We show that interesting insights can be obtained by sampling emerging global trends on Twitter. The study also offers some important insights providing descriptive statistics and graphical description from the preliminary experiments.

A composite random walk for facing environmental uncertainty and reduced perceptual capabilities

Theoretical and empirical studies in Biology have showed that strategies based on different random walks, such as: Brownian random walk and Levy random walk are the best option when there is some degree of environmental uncertainty and there is a lack of perceptual capabilities. When a random walker has no information about where targets are located, different systematic or random searches may provide different chances to find them. However, when time consumption, energy cost and malfunction risks are determinants, an adaptive search strategy becomes necessary in order to improve the performance of the strategy. Thus, we can use a practical methodology to combine a systematic search with a random search through a biological fluctuation. We demonstrate that, in certain environments it is possible to combine a systematic search with a random search to optimally cover a given area. Besides, this work improves the search performance in comparison with pure random walks such as Brownian walk and Levy walk. We show these theoretical results using computer simulations.

A Hybrid Methodology for Robotic Architectures with a Cellular Approach

The clear inclination that has come showing the bio-inspired investigation has been a great motivation for the elaboration of this article, where it is tried to propose a reactive robotic methodology from the cellular point of view. The approach for this research was inspired by DNA computing and molecular biology, this research show that this biological inspiration can be used as a mature robotic methodology, which, takes bases from traditional architectures such as the architecture of subsumption of Rodney Brooks and the methodology of potential fields. We provide a revision of this methodology comparing it with the Principles of Design of Rolf Pfeifer, as well as with the General Computing Framework proposed by Claude and Paun. The purpose of this preview is to make hints for a future work that includes two paradigms (robotic with DNA computing)

Collecting Random Samples from Facebook: An Efficient Heuristic for Sampling Large and Undirected Graphs via a Metropolis-Hastings Random Walk

The lack of a sampling frame (i.e., a complete list of users) for most Online Social Networks (OSNs) makes sampling methods especially difficult. Thus, reliable and efficient sampling methods are essential for practical estimation of OSN properties. Recent work in this area has thus focused on sampling methods that allow precise inference from a relatively large-scale social networks such as Facebook. We propose a sampling method on OSNs, based on a Metropolis-Hastings Random Walk (MHRW) algorithm. In this regard, we have developed a social explorer in order to collect random samples from Facebook. In addition, we address the question whether different probability distributions may be able to alter the behavior of the MHRW and enhance the effectiveness of yielding a representative sample. Thus, in this paper, we seek to understand whether the MHRW algorithm can be exploited by switching the random generator to provide better results. We evaluated the performance of our MHRW algorithm providing a descriptive statistics of the collected data. Moreover, we sketch the collecting procedure carried out on Facebook in real-time. Finally, we provide a formal convergence analysis to evaluate whether the sample of draws has attained an equilibrium state to get a rough estimate of the sample quality.

Scraping global threats in Facebook through movement patterns generated by random walks

Predicting the occurrence of emerging global threats through Open Social Networks has become a paramount task for national security agencies. We study a set of four foraging strategies to provide an automated searching across the Facebook social network. Our work shows how movement patterns generated by random walks can be developed and applied as novel choices for facing a complex environment, e.g. the Facebook social graph. We develop four algorithms based on optimal foraging theory for crawling the social network and gathering publicly accessible data. We also use our random walks with the aim of sampling and collecting open information through millions of Facebook messages. Finally, this approach allows us to glean insights into the collective moods of regions or groups abroad.

Using sequences of knots as a random search

Fink and Mao define a knot as “a sequence of moves creating an aesthetic structure or topology, where its properties are preserved under continuous deformations” [1]. Thus, it is possible to emulate a random search behavior [5], using a set of steps that represents a knot. However, a single knot is not enough to cover a specific area, due to this lack of coverage, we suggest link several knots in order to increase the searching scope.

Comparing Three Simulated Strategies for Cancer Monitoring with Nanorobots

The use of nanorobots in medical applications, specifically cancer treatment, is a serious alternative to prevent this disease. Locating chemical sources and tracking them over time, are tasks where nanorobotics is an ideal candidate to accomplish them. We present a multiagent simulation of three bio-inspired strategies to find targets in fluid environments; including diverse conditions for example: noisy sensors, interference between agents and obstacles generated by the environment itself. Besides, we present a comparative analysis among the three strategies. The results show that nanorobotics used in cancer therapy needs to explore an extensive range of blind searching techniques without communication.

Political Participation in Mexico through Twitter

We used survey data and collected data from the Online Social Network (OSN) Twitter between October the 5th and November the 9th (time window) to provide an overview related to political participation in Mexico. With the survey data we provided a qualitative assessment of political participation in Mexico by examining electoral participation, levels of political participation between regions, Mexicans’ interest in politics and their sources of political information. With our collected data, we described the intensity of political participation in this OSN, we identified locations of high Twitter activity and identified political movements including agencies behind them. With this information, we compare and contrast political participation in Mexico to its counterpart through Twitter. We show that political participation in Mexico seems to be decreasing. However, according to our preliminary results political participation in Mexico through Twitter seems to be increasing. In this regard, our research points towards the emergence of Twitter as a significant platform in terms of political participation in Mexico. Our study analyses the impact of how different agencies related to social movements can enhance political participation trough Twitter. We show that emergent topics related to political participation in Mexico are important because they could help to explore how politics becomes of public interest. The study also offers some important insights for studying the type of political content that users are more likely to tweet.

Sampling Emerging Social Behavior in Facebook Using Random Walk Models

It has long been recognized that random walk models apply to a great diversity of situations such as: economics, mathematics and biophysics; current trends about Open Social Networks require new approaches for analyzing material publicly accessible. Thus, in this chapter we examine the potential of random walks to further our understanding about monitoring Social Behavior, taking Facebook as a case study. Although most of the work related to random walk models is traditionally used to generate animal movement paths, it is also possible to adapt classic diffusion models into exploratory algorithms with the aim to improve the ability to search under a complex environment. This algorithmic abstraction provides an analogy for a dissipative process within which trajectories are drawn through the virtual nodes of Facebook.