Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.

DeepInf: Social Influence Prediction with Deep Learning

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.

pdf/a-survey-of-information-cascade-analysis-models-predictions-1w3xvl96t3.pdf

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. 
Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.

Telegram is a new Instant Messaging application providing key features for both public and private messaging. Telegram is similar to group broadcast or micro-blogging platforms, while on the other hand, it has features of ordinary Instant Messaging applications such as WhatsApp. In this paper, investigating a real dataset crawled from Telegram, we provide several observations which can explain the information flow, business model of content providers, and social sensing aspects of Telegram. The crawled dataset which is manually labeled by six persons contains two months of public messages of selected Telegram channels. Moreover, we introduce the viral messages in instant messaging services and propose formal definition of these messages as well as deeply analyzing their characteristics and features. Detection of virality characteristics of messages in Telegram can be beneficial for both end-users and digital marketers. Consequently, we propose statistical and word embedding approaches to detect viral messages and their sentiment and message category.Our experiments indicate that the word embedding approach can significantly outperform other baseline models.

Characteristics of viral messages on Telegram; The world’s largest hybrid public and private messenger

Social influence is a fundamental issue in social network analysis and has attracted tremendous attention with the rapid growth of online social networks. However, existing research mainly focuses on studying peer influence. This paper introduces a novel notion of structural influence and studies how to efficiently discover structural influence patterns from social streams. We present three sampling algorithms with theoretical unbiased guarantee to speed up the discovery process. Experiments on a big microblogging dataset show that the proposed sampling algorithms can achieve a 10 times speedup compared to the exact influence pattern mining algorithm, with an average error rate of only 1.0%. The extracted structural influence patterns have many applications. We apply them to predict retweet behavior, with performance being significantly improved.

StructInf: Mining structural influence from social streams

Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life - they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging - their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform - the largest standalone messaging communication service in China - with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and short-term groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users' daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from group-level perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members' historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.

The Lifecycle and Cascade of WeChat Social Messaging Groups

Social instant messaging services are emerging as a transfo mative form with which people connect, communicate with friends in their daily life — they catalyze the formation of social groups, an d they bring people stronger sense of community and connection. Ho wever, research community still knows little about the forma tion and evolution of groups in the context of social messaging — thei r lif cycles, the change in their underlying structures over time , and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform — the largest standalone messagin g communication service in China — with the goal of understand ing the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifec ycle, and develop a separability model by taking into account a bro d range of group-level features, showing that long-term and s hortterm groups are inherently distinct. We also found that the l ifecycle of messaging groups is largely dependent on their social rol es and functions in users’ daily social experiences and specific pu rposes. Given the strong separability between the long-term and sho rt-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from grouplevel perspective, we investigate the individual-level at tributes of group members and study the diffusion process by which group s gain new members. By considering members’ historical engag ement behavior as well as the local social network structure t hat they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.

The Lifecycle and Cascade of Social Messaging Groups.

The invention discloses a feature acquisition method and device for a social group, belonging to the technical field of big data. The method includes: obtaining an invitation relationship record and afriend relationship record of the social group; creating a group network structure; according to at least one of a user distribution feature and a cascade tree feature of the group network structure,obtaining a designated feature of the social group, the user distribution feature being used for representing the distribution of a member user and a friend user, and the cascade tree feature being used for representing the distribution of an inviter member user and an invitee member user. Not just only on the basis of the attribute feature of the social group, the designated feature of the social group is obtained on the basis of at least one of the user distribution feature of the social group and the cascade tree feature, in this way, the attribute feature of the social group is expanded,the diversity is improved, and the prediction accuracy is improved when the method and the device are applied to prediction of the lifetime of the social group.

Feature acquisition method and device for social group

—Graph embedding provides a feasible methodology to conduct pattern classiﬁcation for graph-structured data by mapping each data into the vectorial space. Various pioneering works are essentially coding method that concentrates on a vectorial representation about the inner properties of a graph in terms of the topological constitution, node attributions, link relations, etc. However, the classiﬁcation for each targeted data is a qualitative issue based on understanding the overall discrepancies within the dataset scale. From the statistical point of view, these discrepancies manifest a metric distribution over the dataset scale if the distance metric is adopted to measure the pairwise similarity or dissimilarity. Therefore, we present a novel embedding strategy named MetricDistribution2vec to extract such distribution characteristics into the vectorial representation for each data. We demonstrate the application and effectiveness of our representation method in the supervised prediction tasks on extensive real-world structural graph datasets. The results have gained some unexpected increases compared with a surge of baselines on all the datasets, even if we take the lightweight models as classiﬁers. Moreover, the proposed methods also conducted experiments in Few-Shot classiﬁcation scenarios, and the results still show attractive discrimination in rare training samples based inference.

pdf/metric-distribution-to-vector-constructing-data-snugz2gn.pdf

Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies

The present application relates to an invitation behavior prediction method and apparatus, and a storage medium, belonging to the field of internet technology. The method comprises: acquiring group behavior feature information of a first user; acquiring group relationship feature information of a second user; inputting group architecture feature information of a group, the group behavior feature information of the first user, and the group relationship feature information of the second user to an invitation prediction model so as to obtain a target member user and a candidate member user of the target member user; and sending invitation prediction information to the target member user, the invitation prediction information being used to prompt the target member user to add the candidate member user of the target member user to the group. The present application can predict invitation behavior on the basis of an invitation prediction model trained from features representing an intra-group member user relationship and invitation behavior of member users, thereby achieving a more optimal intelligent social media application and maximizing the utilization of interactional functions provided by the group in the social media application.

Invitation behavior prediction method and apparatus, and storage medium

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