8 research outputs found
STAND: A Spatio-Temporal Algorithm for Network Diffusion Simulation
Information, ideas, and diseases, or more generally, contagions, spread over
space and time through individual transmissions via social networks, as well as
through external sources. A detailed picture of any diffusion process can be
achieved only when both a good network structure and individual diffusion
pathways are obtained. The advent of rich social, media and locational data
allows us to study and model this diffusion process in more detail than
previously possible. Nevertheless, how information, ideas or diseases are
propagated through the network as an overall process is difficult to trace.
This propagation is continuous over space and time, where individual
transmissions occur at different rates via complex, latent connections.
To tackle this challenge, a probabilistic spatiotemporal algorithm for
network diffusion (STAND) is developed based on the survival model in this
research. Both time and spatial distance are used as explanatory variables to
simulate the diffusion process over two different network structures. The aim
is to provide a more detailed measure of how different contagions are
transmitted through various networks where nodes are geographic places at a
large scale
Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibility
is limited to its local neighborhood? This problem arises in numerous
real-world applications, such as surveying and task routing in social networks,
team formation in collaborative networks and experimental design with
dependency constraints. Often the informativeness of a set of nodes can be
quantified via a submodular utility function. Existing approaches for
submodular optimization, however, require that the set of all nodes that can be
selected is known ahead of time, which is often unrealistic. In contrast, we
propose a novel model where we start our exploration from an initial node, and
new nodes become visible and available for selection only once one of their
neighbors has been chosen. We then present a general algorithm NetExp for this
problem, and provide theoretical bounds on its performance dependent on
structural properties of the underlying network. We evaluate our methodology on
various simulated problem instances as well as on data collected from social
question answering system deployed within a large enterprise.Comment: Longer version of IJCAI'15 pape
The analysis of interlocking directors via hypergraphs
Since the dawn of the modern era, the associations that link companies together in the gathering and control networks have been interconnected. At the level of corporate governance, companies are linked by common stock directors, joint shareholders and joint directors (interlocking directors). Although often depicted as atomic, individual, unconnected market actors are actually embedded in such networks. In this study, we examine the network characteristics of interlocking directors of Turkish firms listed in ISO 500 and Borsa Istanbul Stock Exchange. To capture the higher order relations, we use hypergraphs to model interlocking directors and their relations. By introducing a simple graph representation based on the connectedness of agents in a hyper-network, we also give the community structure that is a cluster of densely connected nodes. The results we obtain in this study indicate that companies of Turkish market operating in global scale have central positions in their interlocking director network.Publisher's Versio
The role of geography in the complex diffusion of innovations
The urban-rural divide is increasing in modern societies calling for
geographical extensions of social influence modelling. Improved understanding
of innovation diffusion across locations and through social connections can
provide us with new insights into the spread of information, technological
progress and economic development. In this work, we analyze the spatial
adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and
uncover empirical features about the spatial adoption in social networks.
During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million
friendship ties of 3 million users. We find that the number of adopters as a
function of town population follows a scaling law that reveals a strongly
concentrated early adoption in large towns and a less concentrated late
adoption. We also discover a strengthening distance decay of spread over the
life-cycle indicating high fraction of distant diffusion in early stages but
the dominance of local diffusion in late stages. The spreading process is
modelled within the Bass diffusion framework that enables us to compare the
differential equation version with an agent-based version of the model run on
the empirical network. Although both models can capture the macro trend of
adoption, they have limited capacity to describe the observed trends of urban
scaling and distance decay. We find, however that incorporating adoption
thresholds, defined by the fraction of social connections that adopt a
technology before the individual adopts, improves the network model fit to the
urban scaling of early adopters. Controlling for the threshold distribution
enables us to eliminate the bias induced by local network structure on
predicting local adoption peaks. Finally, we show that geographical features
such as distance from the innovation origin and town size influence prediction
of adoption peak at local scales.Comment: 21 pages, 11 figures, 4 table
Urban hierarchy and spatial diffusion over the innovation life cycle
Successful innovations achieve large geographical coverage by spreading
across settlements and distances. For decades, spatial diffusion has been
argued to take place along the urban hierarchy such that the innovation first
spreads from large to medium cities then later from medium to small cities.
Yet, the role of geographical distance, the other major factor of spatial
diffusion, was difficult to identify in hierarchical diffusion due to missing
data on spreading events. In this paper, we exploit spatial patterns of
individual invitations on a social media platform sent from registered users to
new users over the entire life cycle of the platform. This enables us to
disentangle the role of urban hierarchy and the role of distance by observing
the source and target locations of flows over an unprecedented timescale. We
demonstrate that hierarchical diffusion greatly overlaps with diffusion to
close distances and these factors co-evolve over the life cycle; thus, their
joint analysis is necessary. Then, a regression framework is applied to
estimate the number of invitations sent between pairs of towns by years in the
life cycle with the population sizes of the source and target towns, their
combinations, and the distance between them. We confirm that hierarchical
diffusion prevails initially across large towns only but emerges in the full
spectrum of settlements in the middle of the life cycle when adoption
accelerates. Unlike in previous gravity estimations, we find that after an
intensifying role of distance in the middle of the life cycle a surprisingly
weak distance effect characterizes the last years of diffusion. Our results
stress the dominance of urban hierarchy in spatial diffusion and inform future
predictions of innovation adoption at local scales
A bitwise clique detection approach for accelerating power graph computation and clustering dense graphs
Graphs are at the essence of many data representations. The visual analytics over graphs is usually difficult due to their size, which makes their visual display challenging, and their fundamental algorithms, which are often classified as NP-hard problems. The Power Graph Analysis (PGA) is a method that simplifies networks using reduced representations for complete subgraphs (cliques) and complete bipartite subgraphs (bicliques), in both cases with edge reductions. The benefits of a power graph are the preservation of information and its capacity to show essential information about the original network. However, finding an optimal representation (maximum edges reduction) is also an NPhard problem. In this work, we propose BCD, a greedy algorithm that uses a Bitwise Clique Detection approach to finding power graphs. BCD is faster than competing strategies and allows the analysis of bigger graphs. For the display of larger power graphs, we propose an orthogonal layout to prevent overlapping of edges and vertices. Finally, we describe how the structure induced by the power graph is used for clustering analysis of dense graphs. We demonstrate with several datasets the results obtained by our proposal and compare against competing strategies.Os grafos são essenciais para muitas representações de dados. A análise visual de grafos é usualmente difícil devido ao tamanho, o que representa um desafio para sua visualização. Além de isso, seus algoritmos fundamentais são frequentemente classificados como NP-difícil. Análises dos grafos de potência (PGA em inglês) é um método que simplifica redes usando representações reduzidas para subgrafos completos chamados cliques e subgrafos bipartidos chamados bicliques, em ambos casos com una redução de arestas. Os benefícios da representação de grafo de potência são a preservação de informação e a capacidade de mostrar a informação essencial sobre a rede original. Entretanto, encontrar uma representação ótima (a máxima redução de arestas possível) é também um problema NP-difícil. Neste trabalho, propomos BCD, um algoritmo guloso que usa um abordagem de detecção de bicliques baseado em operações binarias para encontrar representações de grafos de potencia. O BCD é mas rápido que as estratégias atuais da literatura. Finalmente, descrevemos como a estrutura induzida pelo grafo de potência é utilizado para as análises dos grafos densos na detecção de agrupamentos de nodos