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At least 703 records · Page 39Linked to original sources
arXiv2013

Nonparametric Multi-group Membership Model for Dynamic Networks

Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure of groups. We demonstrate our model's capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction.

cs.SI↗
arXiv2013

On Scaling Functionality in Urban Form

Assuming that the ultimate purpose of the city is to provide support to human interaction and that opportunities to that social interaction are unevenly distributed across the urban fabric, this paper reports some attempts to describe such a distribution, as well as to infer the role of urban form in it. In order to do that, it is proposed, firstly, a method to describe urban form from its smallest components up to the different urban fabric patches, to the entire spatial system, and second, a model to represent social interaction as a process associated to the urban morphology. Both the spatial description and the analytical model are discussed through the examination of some results, obtained through simulation.

physics.soc-ph↗
arXiv2013

A Semi-automated Peer-review System

A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed.

cs.DL↗
arXiv2013

Social Networks and Collective Intelligence: A Return to the Agora

Nowadays, acquisition of trustable information is increasingly important in both professional and private contexts. However, establishing what information is trustable and what is not, is a very challenging task. For example, how can information quality be reliably assessed? How can sources? credibility be fairly assessed? How can gatekeeping processes be found trustworthy when filtering out news and deciding ranking and priorities of traditional media? An Internet-based solution to a human-based ancient issue is being studied, and it is called Polidoxa, from Greek "poly", meaning "many" or "several" and "doxa", meaning "common belief" or "popular opinion". This old problem will be solved by means of ancient philosophies and processes with truly modern tools and technologies. This is why this work required a collaborative and interdisciplinary joint effort from researchers with very different backgrounds and institutes with significantly different agendas. Polidoxa aims at offering: 1) a trust-based search engine algorithm, which exploits stigmergic behaviours of users? network, 2) a trust-based social network, where the notion of trust derives from network activity and 3) a holonic system for bottom-up self-protection and social privacy. By presenting the Polidoxa solution, this work also describes the current state of traditional media as well as newer ones, providing an accurate analysis of major search engines such as Google and social network (e.g., Facebook). The advantages that Polidoxa offers, compared to these, are also clearly detailed and motivated. Finally, a Twitter application (Polidoxa@twitter) which enables experimentation of basic Polidoxa principles is presented.

cs.SI↗
arXiv2013

Selection Effects in Online Sharing: Consequences for Peer Adoption

Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt. We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.

cs.SI↗
arXiv2013

Practical Characterization of Large Networks Using Neighborhood Information

Characterizing large online social networks (OSNs) through node querying is a challenging task. OSNs often impose severe constraints on the query rate, hence limiting the sample size to a small fraction of the total network. Various ad-hoc subgraph sampling methods have been proposed, but many of them give biased estimates and no theoretical basis on the accuracy. In this work, we focus on developing sampling methods for OSNs where querying a node also reveals partial structural information about its neighbors. Our methods are optimized for NoSQL graph databases (if the database can be accessed directly), or utilize Web API available on most major OSNs for graph sampling. We show that our sampling method has provable convergence guarantees on being an unbiased estimator, and it is more accurate than current state-of-the-art methods. We characterize metrics such as node label density estimation and edge label density estimation, two of the most fundamental network characteristics from which other network characteristics can be derived. We evaluate our methods on-the-fly over several live networks using their native APIs. Our simulation studies over a variety of offline datasets show that by including neighborhood information, our method drastically (4-fold) reduces the number of samples required to achieve the same estimation accuracy of state-of-the-art methods.

cs.SI↗
arXiv2013

A Fractal and Scale-free Model of Complex Networks with Hub Attraction Behaviors

It is widely believed that fractality of complex networks origins from hub repulsion behaviors (anticorrelation or disassortativity), which means large degree nodes tend to connect with small degree nodes. This hypothesis was demonstrated by a dynamical growth model, which evolves as the inverse renormalization procedure proposed by Song et al. Now we find that the dynamical growth model is based on the assumption that all the cross-boxes links has the same probability e to link to the most connected nodes inside each box. Therefore, we modify the growth model by adopting the flexible probability e, which makes hubs have higher probability to connect with hubs than non-hubs. With this model, we find some fractal and scale-free networks have hub attraction behaviors (correlation or assortativity). The results are the counter-examples of former beliefs.

physics.soc-ph↗
arXiv2013

A Comparison of Tram Priority at Signalized Intersections

We study tram priority at signalized intersections using a stochastic cellular automaton model for multimodal traffic flow. We simulate realistic traffic signal systems, which include signal linking and adaptive cycle lengths and split plans, with different levels of tram priority. We find that tram priority can improve service performance in terms of both average travel time and travel time variability. We consider two main types of tram priority, which we refer to as full and partial priority. Full tram priority is able to guarantee service quality even when traffic is saturated, however, it results in significant costs to other road users. Partial tram priority significantly reduces tram delays while having limited impact on other traffic, and therefore achieves a better result in terms of the overall network performance. We also study variations in which the tram priority is only enforced when trams are running behind schedule, and we find that those variations retain almost all of the benefit for tram operations but with reduced negative impact on the network.

nlin.CG↗
arXiv2013

Exploiting Direct and Indirect Information for Friend Suggestion in ZingMe

Friend suggestion is a fundamental problem in social networks with the goal of assisting users in creating more relationships, and thereby enhances interest of users to the social networks. This problem is often considered to be the link prediction problem in the network. ZingMe is one of the largest social networks in Vietnam. In this paper, we analyze the current approach for the friend suggestion problem in ZingMe, showing its limitations and disadvantages. We propose a new efficient approach for friend suggestion that uses information from the network structure, attributes and interactions of users to create resources for the evaluation of friend connection amongst users. Friend connection is evaluated exploiting both direct communication between the users and information from other ones in the network. The proposed approach has been implemented in a new system version of ZingMe. We conducted experiments, exploiting a dataset derived from the users' real use of ZingMe, to compare the newly proposed approach to the current approach and some well-known ones for the accuracy of friend suggestion. The experimental results show that the newly proposed approach outperforms the current one, i.e., by an increase of 7% to 98% on average in the friend suggestion accuracy. The proposed approach also outperforms other ones for users who have a small number of friends with improvements from 20% to 85% on average. In this paper, we also discuss a number of open issues and possible improvements for the proposed approach.

cs.SI↗
arXiv2013

Sampling Content Distributed Over Graphs

Despite recent effort to estimate topology characteristics of large graphs (i.e., online social networks and peer-to-peer networks), little attention has been given to develop a formal methodology to characterize the vast amount of content distributed over these networks. Due to the large scale nature of these networks, exhaustive enumeration of this content is computationally prohibitive. In this paper, we show how one can obtain content properties by sampling only a small fraction of vertices. We first show that when sampling is naively applied, this can produce a huge bias in content statistics (i.e., average number of content duplications). To remove this bias, one may use maximum likelihood estimation to estimate content characteristics. However our experimental results show that one needs to sample most vertices in the graph to obtain accurate statistics using such a method. To address this challenge, we propose two efficient estimators: special copy estimator (SCE) and weighted copy estimator (WCE) to measure content characteristics using available information in sampled contents. SCE uses the special content copy indicator to compute the estimate, while WCE derives the estimate based on meta-information in sampled vertices. We perform experiments to show WCE and SCE are cost effective and also ``{\em asymptotically unbiased}''. Our methodology provides a new tool for researchers to efficiently query content distributed in large scale networks.

cs.SI↗
arXiv2013

The Stabilizing Role of Global Alliances in the Dynamics of Coalition Forming

Coalition forming is investigated among countries, which are coupled with short range interactions, under the influence of external fields produced by the existence of global alliances. The model rests on the natural model of coalition forming inspired from Statistical Physics, where instabilities are a consequence of decentralized maximization of the individual benefits of actors within their long horizon of rationality as the ability to envision a way through intermediate loosing states, to a better configuration. The effects of those external incentives on the interactions between countries and the eventual stabilization of coalitions are studied. The results shed a new light on the understanding of the complex phenomena of stabilization and fragmentation in the coalition dynamics and on the possibility to design stable coalitions. In addition to the formal implementation of the model, the phenomena is illustrated through some historical cases of conflicts in Western Europe.

physics.soc-ph↗
arXiv2013

Percolation on the institute-enterprise R&D collaboration networks

Realistic network-like systems are usually composed of multiple networks with interacting relations such as school-enterprise research and development collaboration networks. Here we study the percolation properties of a special kind of that R&D collaboration networks, namely institute-enterprise R&D collaboration networks. We introduce two actual IERDCNs to show their structural properties, and present a mathematical framework based on generating functions for analyzing an interacting network with any connection probability. Then we illustrate the percolation threshold and structural parameter arithmetic in the sub-critical and supercritical regimes. We compare the predictions of our mathematical framework and arithmetic to data for two real R&D collaboration networks and a number of simulations, and we find that they are in remarkable agreement with the data. We show applications of the framework to electronics R&D collaboration networks.

physics.soc-ph↗
arXiv2013

Relative importance of social synergy, assortation and networks in the evolution of social cooperation

We compare the likelihood of different socially relevant features to allow the evolutionary emergence and maintenance of cooperation in a generalized variant of the iterated Prisoners Dilemma game. Results show that the average costs/benefit balance of cooperation is the primary constraint for its establishment and maintenance. Behavior increasing inclusive fitness such as assortation, homophily, kin-selection and tagging of individuals, is second in importance. Networks characteristics were the least important in favoring the establishment and maintenance of cooperation, despite being the most popular in recent research on the subject. Results suggest that inclusive fitness theory with its expansions to include assortative and economic considerations is more general, powerful and relevant in analyzing social phenomena than kin selection theory with its emphasis on genetic relatedness. Merging economics with evolutionary theory will be necessary to reveal more about the nature of social dynamics.

physics.soc-ph↗
arXiv2013

A Trust Model Based Analysis of Social Networks

In this paper, we analyse the sustainability of social networks using STrust, our social trust model. The novelty of the model is that it introduces the concept of engagement trust and combines it with the popularity trust to derive the social trust of the community as well as of individual members in the community. This enables the recommender system to use these different types of trust to recommend different things to the community, and identify (and recommend) different roles. For example, it recommends mentors using the engagement trust and leaders using the popularity trust. We then show the utility of the model by analysing data from two types of social networks. We also study the sustainability of a community through our social trust model. We observe that a 5% drop in highly trusted members causes more than a 50% drop in social capital that, in turn, raises the question of sustainability of the community. We report our analysis and its results.

cs.SI↗
arXiv2013

Graph measures and network robustness

Network robustness research aims at finding a measure to quantify network robustness. Once such a measure has been established, we will be able to compare networks, to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks. In this paper we survey a large amount of robustness measures on simple, undirected and unweighted graphs, in order to offer a tool for network administrators to evaluate and improve the robustness of their network. The measures discussed in this paper are based on the concepts of connectivity (including reliability polynomials), distance, betweenness and clustering. Some other measures are notions from spectral graph theory, more precisely, they are functions of the Laplacian eigenvalues. In addition to surveying these graph measures, the paper also contains a discussion of their functionality as a measure for topological network robustness.

cs.DM↗
arXiv2013

Human Mobility and Predictability enriched by Social Phenomena Information (extended abstract)

The information collected by mobile phone operators can be considered as the most detailed information on human mobility across a large part of the population. The study of the dynamics of human mobility using the collected geolocations of users, and applying it to predict future users' locations, has been an active field of research in recent years. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location.

cs.SI↗
arXiv2013

Influence Diffusion in Social Networks under Time Window Constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $λ$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $λ$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete graphs.

cs.DS↗