30,779 research outputs found
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
The deluge of digital information in our daily life -- from user-generated
content, such as microblogs and scientific papers, to online business, such as
viral marketing and advertising -- offers unprecedented opportunities to
explore and exploit the trajectories and structures of the evolution of
information cascades. Abundant research efforts, both academic and industrial,
have aimed to reach a better understanding of the mechanisms driving the spread
of information and quantifying the outcome of information diffusion. This
article presents a comprehensive review and categorization of information
popularity prediction methods, from feature engineering and stochastic
processes, through graph representation, to deep learning-based approaches.
Specifically, we first formally define different types of information cascades
and summarize the perspectives of existing studies. We then present a taxonomy
that categorizes existing works into the aforementioned three main groups as
well as the main subclasses in each group, and we systematically review
cutting-edge research work. Finally, we summarize the pros and cons of existing
research efforts and outline the open challenges and opportunities in this
field.Comment: Author version, with 43 pages, 9 figures, and 11 table
Generalized network community detection
Community structure is largely regarded as an intrinsic property of complex
real-world networks. However, recent studies reveal that networks comprise even
more sophisticated modules than classical cohesive communities. More precisely,
real-world networks can also be naturally partitioned according to common
patterns of connections between the nodes. Recently, a propagation based
algorithm has been proposed for the detection of arbitrary network modules. We
here advance the latter with a more adequate community modeling based on
network clustering. The resulting algorithm is evaluated on various synthetic
benchmark networks and random graphs. It is shown to be comparable to current
state-of-the-art algorithms, however, in contrast to other approaches, it does
not require some prior knowledge of the true community structure. To
demonstrate its generality, we further employ the proposed algorithm for
community detection in different unipartite and bipartite real-world networks,
for generalized community detection and also predictive data clustering
Deep Learning on Graphs: A Survey
Deep learning has been shown to be successful in a number of domains, ranging
from acoustics, images, to natural language processing. However, applying deep
learning to the ubiquitous graph data is non-trivial because of the unique
characteristics of graphs. Recently, substantial research efforts have been
devoted to applying deep learning methods to graphs, resulting in beneficial
advances in graph analysis techniques. In this survey, we comprehensively
review the different types of deep learning methods on graphs. We divide the
existing methods into five categories based on their model architectures and
training strategies: graph recurrent neural networks, graph convolutional
networks, graph autoencoders, graph reinforcement learning, and graph
adversarial methods. We then provide a comprehensive overview of these methods
in a systematic manner mainly by following their development history. We also
analyze the differences and compositions of different methods. Finally, we
briefly outline the applications in which they have been used and discuss
potential future research directions.Comment: Accepted by Transactions on Knowledge and Data Engineering. 24 pages,
11 figure
Viral spread with or without emotions in online community
Diffusion of information and viral content, social contagion and influence
are still topics of broad evaluation. We have studied the information epidemic
in a social networking platform in order compare different campaign setups. The
goal of this work is to present the new knowledge obtained from studying two
artificial (experimental) and one natural (where people act emotionally) viral
spread that took place in a closed virtual world. We propose an approach to
modeling the behavior of online community exposed on external impulses as an
epidemic process. The presented results base on online multilayer system
observation, and show characteristic difference between setups, moreover, some
important aspects of branching processes are presented. We run experiments,
where we introduced viral to system and agents were able to propagate it. There
were two modes of experiment: with or without award. Dynamic of spreading both
of virals were described by epidemiological model and diffusion. Results of
experiments were compared with real propagation process - spontaneous
organization against ACTA. During general-national protest against new
antypiracy multinational agreement - ACTA, criticized for its adverse effect on
e.g. freedom of expression and privacy of communication, members of chosen
community could send a viral such as Stop-ACTA transparent. In this scenario,
we are able to capture behavior of society, when real emotions play a role, and
compare results with artificiality conditioned experiments. Moreover, we could
measure effect of emotions in viral propagation. As theory explaining the role
of emotions in spreading behaviour as an factor of message targeting and
individuals spread emotional-oriented content in a more carefully and more
influential way, the experiments show that probabilities of secondary
infections are four times bigger if emotions play a role
Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks
Researchers have attempted to model information diffusion and topic trends
and lifecycle on online social networks. They have investigated the role of
content, social connections and communities, familiarity and behavioral
similarity in this context. The current article presents a survey of
representative models that perform topic analysis, capture information
diffusion, and explore the properties of social connections in the context of
online social networks. The article concludes with a set of outlines of open
problems and possible directions of future research interest. This article is
intended for researchers to identify the current literature, and explore
possibilities to improve the art
Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
We present Walklets, a novel approach for learning multiscale representations
of vertices in a network. In contrast to previous works, these representations
explicitly encode multiscale vertex relationships in a way that is analytically
derivable.
Walklets generates these multiscale relationships by subsampling short random
walks on the vertices of a graph. By `skipping' over steps in each random walk,
our method generates a corpus of vertex pairs which are reachable via paths of
a fixed length. This corpus can then be used to learn a series of latent
representations, each of which captures successively higher order relationships
from the adjacency matrix.
We demonstrate the efficacy of Walklets's latent representations on several
multi-label network classification tasks for social networks such as
BlogCatalog, DBLP, Flickr, and YouTube. Our results show that Walklets
outperforms new methods based on neural matrix factorization. Specifically, we
outperform DeepWalk by up to 10% and LINE by 58% Micro-F1 on challenging
multi-label classification tasks. Finally, Walklets is an online algorithm, and
can easily scale to graphs with millions of vertices and edges.Comment: 8 pages, ASONAM'1
Full-scale Cascade Dynamics Prediction with a Local-First Approach
Information cascades are ubiquitous in various social networking web sites.
What mechanisms drive information diffuse in the networks? How does the
structure and size of the cascades evolve in time? When and which users will
adopt a certain message? Approaching these questions can considerably deepen
our understanding about information cascades and facilitate various vital
applications, including viral marketing, rumor prevention and even link
prediction. Most previous works focus only on the final cascade size
prediction. Meanwhile, they are always cascade graph dependent methods, which
make them towards large cascades prediction and lead to the criticism that
cascades may only be predictable after they have already grown large. In this
paper, we study a fundamental problem: full-scale cascade dynamics prediction.
That is, how to predict when and which users are activated at any time point of
a cascading process. Here we propose a unified framework, FScaleCP, to solve
the problem. Given history cascades, we first model the local spreading
behaviors as a classification problem. Through data-driven learning, we
recognize the common patterns by measuring the driving mechanisms of cascade
dynamics. After that we present an intuitive asynchronous propagation method
for full-scale cascade dynamics prediction by effectively aggregating the local
spreading behaviors. Extensive experiments on social network data set suggest
that the proposed method performs noticeably better than other state-of-the-art
baselines
Complex networks and human language
This paper introduces how human languages can be studied in light of recent
development of network theories. There are two directions of exploration. One
is to study networks existing in the language system. Various lexical networks
can be built based on different relationships between words, being semantic or
syntactic. Recent studies have shown that these lexical networks exhibit
small-world and scale-free features. The other direction of exploration is to
study networks of language users (i.e. social networks of people in the
linguistic community), and their role in language evolution. Social networks
also show small-world and scale-free features, which cannot be captured by
random or regular network models. In the past, computational models of language
change and language emergence often assume a population to have a random or
regular structure, and there has been little discussion how network structures
may affect the dynamics. In the second part of the paper, a series of
simulation models of diffusion of linguistic innovation are used to illustrate
the importance of choosing realistic conditions of population structure for
modeling language change. Four types of social networks are compared, which
exhibit two categories of diffusion dynamics. While the questions about which
type of networks are more appropriate for modeling still remains, we give some
preliminary suggestions for choosing the type of social networks for modeling
Bayesian Models for Heterogeneous Personalized Health Data
The purpose of this study is to leverage modern technology (such as mobile or
web apps in Beckman et al. (2014)) to enrich epidemiology data and infer the
transmission of disease. Homogeneity related research on population level has
been intensively studied in previous work. In contrast, we develop hierarchical
Graph-Coupled Hidden Markov Models (hGCHMMs) to simultaneously track the spread
of infection in a small cell phone community and capture person-specific
infection parameters by leveraging a link prior that incorporates additional
covariates. We also reexamine the model evolution of the hGCHMM from simple
HMMs and LDA, elucidating additional flexibility and interpretability. Due to
the non-conjugacy of sparsely coupled HMMs, we design a new approximate
distribution, allowing our approach to be more applicable to other application
areas. Additionally, we investigate two common link functions, the
beta-exponential prior and sigmoid function, both of which allow the
development of a principled Bayesian hierarchical framework for disease
transmission. The results of our model allow us to predict the probability of
infection for each person on each day, and also to infer personal physical
vulnerability and the relevant association with covariates. We demonstrate our
approach experimentally on both simulation data and real epidemiological
records.Comment: 35 pages; Heterogeneous Flu Diffusion, Social Networks, Dynamic
Bayesian Modelin
Inferring the mesoscale structure of layered, edge-valued and time-varying networks
Many network systems are composed of interdependent but distinct types of
interactions, which cannot be fully understood in isolation. These different
types of interactions are often represented as layers, attributes on the edges
or as a time-dependence of the network structure. Although they are crucial for
a more comprehensive scientific understanding, these representations offer
substantial challenges. Namely, it is an open problem how to precisely
characterize the large or mesoscale structure of network systems in relation to
these additional aspects. Furthermore, the direct incorporation of these
features invariably increases the effective dimension of the network
description, and hence aggravates the problem of overfitting, i.e. the use of
overly-complex characterizations that mistake purely random fluctuations for
actual structure. In this work, we propose a robust and principled method to
tackle these problems, by constructing generative models of modular network
structure, incorporating layered, attributed and time-varying properties, as
well as a nonparametric Bayesian methodology to infer the parameters from data
and select the most appropriate model according to statistical evidence. We
show that the method is capable of revealing hidden structure in layered,
edge-valued and time-varying networks, and that the most appropriate level of
granularity with respect to the additional dimensions can be reliably
identified. We illustrate our approach on a variety of empirical systems,
including a social network of physicians, the voting correlations of deputies
in the Brazilian national congress, the global airport network, and a proximity
network of high-school students.Comment: 17 pages, 9 figure
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