Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.Comment: 11 pages, 4 figure

Entrograms and coarse graining of dynamics on complex networks

Using an information theoretic point of view, we investigate how a dynamics acting on a network can be coarse grained through the use of graph partitions. Specifically, we are interested in how aggregating the state space of a Markov process according to a partition impacts on the thus obtained lower-dimensional dynamics. We highlight that for a dynamics on a particular graph there may be multiple coarse grained descriptions that capture different, incomparable features of the original process. For instance, a coarse graining induced by one partition may be commensurate with a time-scale separation in the dynamics, while another coarse graining may correspond to a different lower-dimensional dynamics that preserves the Markov property of the original process. Taking inspiration from the literature of Computational Mechanics, we find that a convenient tool to summarise and visualise such dynamical properties of a coarse grained model (partition) is the entrogram. The entrogram gathers certain information-theoretic measures, which quantify how information flows across time steps. These information theoretic quantities include the entropy rate, as well as a measure for the memory contained in the process, i.e., how well the dynamics can be approximated by a first order Markov process. We use the entrogram to investigate how specific macro-scale connection patterns in the state-space transition graph of the original dynamics result in desirable properties of coarse grained descriptions. We thereby provide a fresh perspective on the interplay between structure and dynamics in networks, and the process of partitioning from an information theoretic perspective. We focus on networks that may be approximated by both a core-periphery or a clustered organization, and highlight that each of these coarse grained descriptions can capture different aspects of a Markov process acting on the network.Comment: 17 pages, 6 figue

Submodular Inference of Diffusion Networks from Multiple Trees

Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.Comment: To appear in the 29th International Conference on Machine Learning (ICML), 2012. Website: http://www.stanford.edu/~manuelgr/network-inference-multitree

Modeling the structure and evolution of discussion cascades

We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these sites, a preferential attachment (PA) model with bias to the root can capture the temporal evolution of the observed trees and many of their statistical properties, namely, probability distributions of the branching factors (degrees), subtree sizes and certain correlations. The parameters of the model are learned efficiently using a novel maximum likelihood estimation scheme for PA and provide a figurative interpretation about the communication habits and the resulting discussion cascades on the four different websites.Comment: 10 pages, 11 figure

When resources collide: Towards a theory of coincidence in information spaces

This paper is an attempt to lay out foundations for a general theory of coincidence in information spaces such as the World Wide Web, expanding on existing work on bursty structures in document streams and information cascades. We elaborate on the hypothesis that every resource that is published in an information space, enters a temporary interaction with another resource once a unique explicit or implicit reference between the two is found. This thought is motivated by Erwin Shroedingers notion of entanglement between quantum systems. We present a generic information cascade model that exploits only the temporal order of information sharing activities, combined with inherent properties of the shared information resources. The approach was applied to data from the world's largest online citizen science platform Zooniverse and we report about findings of this case study

Carbon Catcher Design Report

Overview. The design of the overall Carbon Catcher project can be separated into four distinct systems, each of which is assigned a specialized committee. The committee names and responsibilities are listed below: Air Mover The overall goal for the Air Mover committee is the design of the turbine assembly. As the overall goal of the project is to collect and separate carbon dioxide from the air, one of the most important parts is to actually get the air to pass through the carbon-catching membrane. Passive air would not give a significant enough yield rate to make the carbon dioxide collection rate impactful, thus air must be sucked through a vacuum/turbine. Membrane The goal of Membrain is to create a membrane that can filter out CO2 through various methods. These methods are limited, due to there being such variety, to certain techniques and membrane material types that have been decided, prior, by the committee. Most membranes will be geared towards utilizing temperature and pressure along with gaseous speed and flow rate. In addition, examining certain treatments, such as regeneration of material, and replacements will be looked into as well, to see how it fares in sustainability. Carbon Storer The Carbon Storer committee will design a store and transport system for fluid CO2 after it is extracted from the atmosphere. Primary considerations include geological solutions, cost-effective materials, and analysis methods to improve overall capacity and efficiency. Additionally, the committee will select an environmentally and economically sustainable method of recycling the captured CO2. PyControl The PyControl committee will design a series of sensors and actuators, which will primarily support the sequestration and pipeline systems present in the Carbon Storer Committee and direct air capture system in Air Mover. The design can be broken into four control layers: Input/Output, Field Controllers, Data, and Supervisory. Goal The overarching goal of Carbon Catcher is to design a cost-effective, scalable atmospheric carbon dioxide removal system that is capable of being deployed in a variety of urban environments and may fit a variety of different customer requirements or requests

Online reverse discourses? Claiming a space for trans voices

In recent years, online media have offered to trans people helpful resources to create new political, cultural and personal representations of their biographies. However, the role of these media in the construction of their social and personal identities has seldom been addressed. Drawing on the theoretical standpoint of positioning theory and diatextual discourse analysis, this paper discusses the results of a research project about weblogs created by Italian trans women. In particular, the aim of this study was to describe the ways online resources are used to express different definitions and interpretation of transgenderism, transsexuality and gender transitioning. We identified four main positioning strategies: \u201cTransgender\u201d, \u201cTranssexual before being a woman\u201d, \u201cA woman who was born male\u201d and \u201cJust a normal woman\u201d. We conclude with the political implications of the pluralization of narratives about gender non-conformity. Specifically, we will highlight how aspects of neoliberal discourses have been appropriated and rearticulated in the construction of gendered subjectivities

Line graphs as social networks

The line graphs are clustered and assortative. They share these topological features with some social networks. We argue that this similarity reveals the cliquey character of the social networks. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8 x 10^6 people. In particular, sharp maxima of the observed data of the degree dependence of the clustering coefficient C(k) are associated with cliques in the social network.Comment: 11 pages, 4 figure