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