The network structural properties at the micro-level, community-level and macro-level have different contributions to the spreading influence of nodes. The challenge is how to make better use of different structural information while keeping the efficiency of the spreading influence identification algorithm. By taking the micro-level, community-level and macro-level structural information into account, an improved graph convolutional network based algorithm, namely the multi-channel RCNN (M-RCNN) is proposed to identify spreading influence nodes. As we focus on both the efficiency and accuracy of the algorithm, three centralities with low computational complexity are introduced: the sum of neighbors’ degree, the number of communities a node is connected with, and the k -core value. To construct the input of the M-RCNN, we first use the Breadth-first algorithm to extract a fixed-size neighborhood network for each node. Then exploit three matrices to encode the input of nodes rather than simply embedding different levels of structural information into the same matrix, which allows the weights that couple the three structural properties to be learned automatically during the training process. The experiments conducted on nine real-world networks show that, on average, compared with the RCNN algorithm, the accuracy obtained by the M-RCNN outperforms by 9.25%. By conducting efficiency test on nine Barabasi–Albert networks, the results show that the computational complexity of the M-RCNN is close to the RCNN. This work is helpful for deeply understanding the effects of network structure on the graph convolutional network performance. • The graph convolutional network is introduced to identify spreading influence nodes. • The structure properties of networks at multiple levels are taken into account. • The proposed model trained by small networks can make predictions in large networks. • Three-channel inputs are constructed to preserve different structural information. 

Identification of spreading influence nodes via multi-level structural attributes based on the graph convolutional network

The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes. 

/pdf/identifying-spreading-influence-nodes-for-social-networks-lfec5agk.pdf

Identifying spreading influence nodes for social networks

The discovery of influential entities in all kinds of networks (e.g., social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands, or products through viral content. In this work, we present a systematic review across (i) online social influence metrics, properties, and applications and (ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

Modeling Influence with Semantics in Social Networks: A Survey

We propose a method to discover latent topics and visualise large collections of tweets for easy identification and interpretation of topics, and exemplify its use with tweets from a Colombian mass media giant in the period 2014--2019. The latent topic analysis is performed in two ways: with the training of a Latent Dirichlet Allocation model, and with the combination of the FastText unsupervised model to represent tweets as vectors and the implementation of K-means clustering to group tweets into topics. Using a classification task, we found that people respond differently according to the various news topics. The classification tasks consists of the following: given a reply to a news tweet, we train a supervised algorithm to predict the topic of the news tweet solely from the reply. Furthermore, we show how the Colombian peace treaty has had a profound impact on the Colombian society, as it is the topic in which most people engage to show their opinions.

Using machine learning and information visualisation for discovering latent topics in Twitter news

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

We devise a system for measuring influence of Twitter users, which we call InfluenceRank, based on certain features extracted from their Twitter profiles and tweets authored over the duration of two months. As in the real world, influence of a user on social media may be judged by the engagement they drive through the content they publish. For a tweet, engagement can be most obviously measured by the number of retweets (RTs), favourites and replies it gets. Our system comprises of a regression based machine learning approach with InfluenceRank as the predictor variable against the set of our proposed features. The regression model has shown reasonable accuracy despite being fit on limited data.

InfluenceRank: A machine learning approach to measure influence of Twitter users

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InfluenceRank: A machine learning approach to measure influence of Twitter users