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arXiv2013

Ashes 2013 - A network theory analysis of Cricket strategies

We demonstrate in this paper the use of tools of complex network theory to describe the strategy of Australia and England in the recently concluded Ashes 2013 Test series. Using partnership data made available by cricinfo during the Ashes 2013 Test series, we generate batting partnership network (BPN) for each team, in which nodes correspond to batsmen and links represent runs scored in partnerships between batsmen. The resulting network display a visual summary of the pattern of run-scoring by each team, which helps us in identifying potential weakness in a batting order. We use different centrality scores to quantify the performance, relative importance and effect of removing a player from the team. We observe that England is an extremely well connected team, in which lower order batsmen consistently contributed significantly to the team score. Contrary to this Australia showed dependence on their top order batsmen.

physics.soc-ph↗

arXiv2013

Modeling the Dynamics of Infectious Diseases in Different Scale-Free Networks with the Same Degree Distribution

The transmission dynamics of some infectious diseases is related to the contact structure between individuals in a network. We used five algorithms to generate contact networks with different topological structure but with the same scale-free degree distribution. We simulated the spread of acute and chronic infectious diseases on these networks, using SI (Susceptible - Infected) and SIS (Susceptible - Infected - Susceptible) epidemic models. In the simulations, our objective was to observe the effects of the topological structure of the networks on the dynamics and prevalence of the simulated diseases. We found that the dynamics of spread of an infectious disease on different networks with the same degree distribution may be considerably different.

q-bio.PE↗

arXiv2013

Using tf-idf as an edge weighting scheme in user-object bipartite networks

Bipartite user-object networks are becoming increasingly popular in representing user interaction data in a web or e-commerce environment. They have certain characteristics and challenges that differentiates them from other bipartite networks. This paper analyzes the properties of five real world user-object networks. In all cases we found a heavy tail object degree distribution with popular objects connecting together a large part of the users causing significant edge inflation in the projected users network. We propose a novel edge weighting strategy based on tf-idf and show that the new scheme improves both the density and the quality of the community structure in the projections. The improvement is also noticed when comparing to partially random networks.

cs.SI↗

arXiv2013

The Extreme Right Filter Bubble

Due to its status as the most popular video sharing platform, YouTube plays an important role in the online strategy of extreme right groups, where it is often used to host associated content such as music and other propaganda. In this paper, we develop a categorization suitable for the analysis of extreme right channels found on YouTube. By combining this with an NMF-based topic modelling method, we categorize channels originating from links propagated by extreme right Twitter accounts. This method is also used to categorize related channels, which are determined using results returned by YouTube's related video service. We identify the existence of a "filter bubble", whereby users who access an extreme right YouTube video are highly likely to be recommended further extreme right content.

cs.SI↗

arXiv2013

Fast community detection using local neighbourhood search

Communities play a crucial role to describe and analyse modern networks. However, the size of those networks has grown tremendously with the increase of computational power and data storage. While various methods have been developed to extract community structures, their computational cost or the difficulty to parallelize existing algorithms make partitioning real networks into communities a challenging problem. In this paper, we propose to alter an efficient algorithm, the Louvain method, such that communities are defined as the connected components of a tree-like assignment graph. Within this framework, we precisely describe the different steps of our algorithm and demonstrate its highly parallelizable nature. We then show that despite its simplicity, our algorithm has a partitioning quality similar to the original method on benchmark graphs and even outperforms other algorithms. We also show that, even on a single processor, our method is much faster and allows the analysis of very large networks.

physics.soc-ph↗

arXiv2013

Spectral community detection in sparse networks

Spectral methods based on the eigenvectors of matrices are widely used in the analysis of network data, particularly for community detection and graph partitioning. Standard methods based on the adjacency matrix and related matrices, however, break down for very sparse networks, which includes many networks of practical interest. As a solution to this problem it has been recently proposed that we focus instead on the spectrum of the non-backtracking matrix, an alternative matrix representation of a network that shows better behavior in the sparse limit. Inspired by this suggestion, we here make use of a relaxation method to derive a spectral community detection algorithm that works well even in the sparse regime where other methods break down. Interestingly, however, the matrix at the heart of the method, it turns out, is not exactly the non-backtracking matrix, but a variant of it with a somewhat different definition. We study the behavior of this variant matrix for both artificial and real-world networks and find it to have desirable properties, especially in the common case of networks with broad degree distributions, for which it appears to have a better behaved spectrum and eigenvectors than the original non-backtracking matrix.

physics.soc-ph↗

arXiv2013

Digital breadcrumbs: Detecting urban mobility patterns and transport mode choices from cellphone networks

Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed that there are 3.5 (HITS: 3.5 million) million and 4.3 (HITS: 4.4 million) million inter-district passengers by public and private transport, respectively. Along with classifying which transportation connections are weak or underserved, the analysis shows that the mode share of public transport use increases from 38 percent in the morning to 44 percent around mid-day and 52 percent in the evening.

cs.SI↗

arXiv2013

Primacy analysis of the system of Bulgarian cities

We study the primacy in the Bulgarian urban system. Two groups of cities are studied: (i) the whole Bulgaria city system that contains about 250 cities and is studied in the time interval between 2004 and 2011; and (ii) A system of 33 cities, studied over the time interval 1887 till 2010. For these cities the 1946 population was over $10\ 000$ inhabitants. The notion of primacy in the two systems of cities is studied first from the global primacy index of Sheppard [$^1$]. Several (new) additional indices are introduced in order to compensate defects in the Sheppard index. Numerical illustrations are illuminating through the so called "length ratio".

nlin.AO↗

arXiv2013

Ensemble approaches for improving community detection methods

Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by aggregating the information found in an ensemble of community structures. This ensemble can found by re-sampling methods, multiple runs of a stochastic community detection method, or by several different community detection algorithms applied to the same network. The proposed method is evaluated using random networks with community structures and compared with two commonly used community detection methods. The proposed method when applied on a stochastic community detection algorithm performs well with low computational complexity, thus offering both a new approach to community detection and an additional community detection method.

physics.soc-ph↗

arXiv2013

Development Towards Sustainability: How to judge past and proposed policies?

The scientific data about the state of our planet, presented at the 2012 (Rio+20) summit, documented that today's human family lives even less sustainably than it did in 1992. The data indicate furthermore that the environmental impacts from our current economic activities are so large, that we are approaching situations where potentially controllable regional problems can easily lead to uncontrollable global disasters. Assuming that (1) the majority of the human family, once adequately informed, wants to achieve a "sustainable way of life" and (2) that the "development towards sustainability" roadmap will be based on scientific principles, one must begin with unambiguous and quantifiable definitions of these goals. As will be demonstrated, the well known scientific method to define abstract and complex issues by their negation, satisfies these requirements. Following this new approach, it also becomes possible to decide if proposed and actual policies changes will make our way of life less unsustainable, and thus move us potentially into the direction of sustainability. Furthermore, if potentially dangerous tipping points are to be avoided, the transition roadmap must include some minimal speed requirements. Combining the negation method and the time evolution of that remaining natural capital in different domains, the transition speed for a "development towards sustainability" can be quantified at local, regional and global scales. The presented ideas allow us to measure the rate of natural capital depletion and the rate of restoration that will be required if humanity is to avoid reaching a sustainable future by a collapse transition.

physics.soc-ph↗

arXiv2013

Using a complex system approach to address world challenges in Food and Agriculture

World food supply is crucial to the well-being of every human on the planet in the basic sense that we need food to live. It also has a profound impact on the world economy, international trade and global political stability. Furthermore, consumption of certain types and amounts foods can affect health, and the choice of livestock and plants for food production can impact sustainable use of global resources. There are communities where insufficient food causes nutritional deficiencies, and at the same time other communities eating too much food leading to obesity and accompanying diseases. These aspects reflect the utmost importance of agricultural production and conversion of commodities to food products. Moreover, all factors contributing to the food supply are interdependent, and they are an integrative part of the continuously changing, adaptive and interdependent systems in the world around us. The properties of such interdependent systems usually cannot be inferred from the properties of its parts. In addressing current challenges, like the apparent incongruences of obesity and hunger, we have to account for the complex interdependencies among areas such as physics and sociology. This is possible using the complex system approach. It encompasses an integrative multi-scale and inter-disciplinary approach. Using a complex system approach that accounts for the needs of stakeholders in the agriculture and food domain, and determines which research programs will enable these stakeholders to better anticipate emerging developments in the world around them, will enable them to determine effective intervention strategies to simultaneously optimise and safeguard their interests and the interests of the environment.

physics.soc-ph↗

arXiv2013

Shortest Paths in Microseconds

Computing shortest paths is a fundamental primitive for several social network applications including socially-sensitive ranking, location-aware search, social auctions and social network privacy. Since these applications compute paths in response to a user query, the goal is to minimize latency while maintaining feasible memory requirements. We present ASAP, a system that achieves this goal by exploiting the structure of social networks. ASAP preprocesses a given network to compute and store a partial shortest path tree (PSPT) for each node. The PSPTs have the property that for any two nodes, each edge along the shortest path is with high probability contained in the PSPT of at least one of the nodes. We show that the structure of social networks enable the PSPT of each node to be an extremely small fraction of the entire network; hence, PSPTs can be stored efficiently and each shortest path can be computed extremely quickly. For a real network with 5 million nodes and 69 million edges, ASAP computes a shortest path for most node pairs in less than 49 microseconds per pair. ASAP, unlike any previous technique, also computes hundreds of paths (along with corresponding distances) between any node pair in less than 100 microseconds. Finally, ASAP admits efficient implementation on distributed programming frameworks like MapReduce.

cs.DC↗

arXiv2013

Pedestrian, Crowd, and Evacuation Dynamics

This contribution describes efforts to model the behavior of individual pedestrians and their interactions in crowds, which generate certain kinds of self-organized patterns of motion. Moreover, this article focusses on the dynamics of crowds in panic or evacuation situations, methods to optimize building designs for egress, and factors potentially causing the breakdown of orderly motion.

physics.soc-ph↗

arXiv2013

Stochastic Agent-Based Simulations of Social Networks

The rapidly growing field of network analytics requires data sets for use in evaluation. Real world data often lack truth and simulated data lack narrative fidelity or statistical generality. This paper presents a novel, mixed-membership, agentbased simulation model to generate activity data with narrative power while providing statistical diversity through random draws. The model generalizes to a variety of network activity types such as Internet and cellular communications, human mobility, and social network interactions. The simulated actions over all agents can then drive an application specific observational model to render measurements as one would collect in real-world experiments. We apply this framework to human mobility and demonstrate its utility in generating high fidelity traffic data for network analytics.

cs.SI↗

arXiv2013

The Cost of an Epidemic over a Complex Network: A Random Matrix Approach

In this paper we quantify the total economic impact of an epidemic over a complex network using tools from random matrix theory. Incorporating the direct and indirect costs of infection, we calculate the disease cost in the large graph limit for an SIS (Susceptible - Infected - Susceptible) infection process. We also give an upper bound on this cost for arbitrary finite graphs and illustrate both calculated costs using extensive simulations on random and real-world networks. We extend these calculations by considering the total social cost of an epidemic, accounting for both the immunization and disease costs for various immunization strategies and determining the optimal immunization. Our work focuses on the transient behavior of the epidemic, in contrast to previous research, which typically focuses on determining the steady-state system equilibrium.

cs.SI↗

arXiv2013

Revealing the intricate effect of collaboration on innovation

We study the Japan and U.S. patent records of several decades to demonstrate the effect of collaboration on innovation. We find that statistically inventor teams slightly outperform solo inventors while company teams perform equally well as solo companies. By tracking the performance record of individual teams we find that inventor teams' performance generally degrades with more repeat collaborations. Though company teams' performance displays strongly bursty behavior, long-term collaboration does not significantly help innovation at all. To systematically study the effect of repeat collaboration, we define the repeat collaboration number of a team as the average number of collaborations over all the teammate pairs. We find that mild repeat collaboration improves the performance of Japanese inventor teams and U.S. company teams. Yet, excessive repeat collaboration does not significantly help innovation at both the inventor and company levels in both countries. To control for unobserved heterogeneity, we perform a detailed regression analysis and the results are consistent with our simple observations. The presented results reveal the intricate effect of collaboration on innovation, which may also be observed in other creative projects.

physics.soc-ph↗

arXiv2013

Mining for Spatially-Near Communities in Geo-Located Social Networks

Current approaches to community detection in social networks often ignore the spatial location of the nodes. In this paper, we look to extract spatially-near communities in a social network. We introduce a new metric to measure the quality of a community partition in a geolocated social networks called "spatially-near modularity" a value that increases based on aspects of the network structure but decreases based on the distance between nodes in the communities. We then look to find an optimal partition with respect to this measure - which should be an "ideal" community with respect to both social ties and geographic location. Though an NP-hard problem, we introduce two heuristic algorithms that attempt to maximize this measure and outperform non-geographic community finding by an order of magnitude. Applications to counter-terrorism are also discussed.

cs.SI↗

arXiv2013

A Scalable Heuristic for Viral Marketing Under the Tipping Model

In a "tipping" model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial "seed" set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 hours. Our experiments also indicate that our algorithm provides small seed sets even if high-degree nodes are removed. Lastly, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend's ability to spread under the tipping model.

cs.SI↗