Abstract
Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other. Social networks have become so indispensable in our everyday life that we cannot imagine what daily life would be like without social networks. Through social networks, we can access friends’ opinions and behaviors easily and are influenced by them in turn. Thus, an effective algorithm to find the top-K influential nodes (the problem of influence maximization) in the social network is critical for various downstream tasks such as viral marketing, anticipating natural hazards, reducing gang violence, public opinion supervision, etc. Solving the problem of influence maximization in real-world propagation scenarios often involves estimating influence strength (influence probability between two nodes), which cannot be observed directly. To estimate influence strength, conventional approaches propose various humanly devised rules to extract features of user interactions, the effectiveness of which heavily depends on domain expert knowledge. Besides, they are often applicable for special scenarios or specific diffusion models. Consequently, they are difficult to generalize into different scenarios and diffusion models. Inspired by the powerful ability of neural networks in the field of representation learning, we designed a hierarchical generative embedding model (HGE) to map nodes into latent space automatically. Then, with the learned latent representation of each node, we proposed a HGE-GA algorithm to predict influence strength and compute the top-K influential nodes. Extensive experiments on real-world attributed networks demonstrate the outstanding superiority of the proposed HGE model and HGE-GA algorithm compared with the state-of-the-art methods, verifying the effectiveness of the proposed model and algorithm.
Highlights
Fueled by the spectacular growth of the internet and Internet of Things, plenty of social networks such as Facebook, Twitter and WeChat have sprung up, changed the mode of interaction between people, and accelerated the development of viral marketing.Originally from the idea of word-of-mouth advertising, viral marketing takes advantage of trust among close social circles of friends, colleagues or families to promote a new product, i.e., when a friend relationship affects a user making decisions on item selection [1,2].Motivated by applications to early viral marketing, a new study area of influence diffusion has thrived
We quantitatively evaluate the performance of the proposed hierarchical generative embedding model (HGE) model in downstream learning tasks and compare the proposed algorithm, HGE-GA, with the state-of-art influence maximization algorithms using the metric of expected spread on several large-scale real-world datasets
GraphSAGE [40]: an attributed network embedding model which leverages node attributes and generates embeddings by sampling and aggregating features from a node’s local neighborhood; AANE [41]: a model which learns attributed network embedding efficiently by decomposing complex modeling and optimization into sub-problems; M-NMF [27]: a single-layer community structure preserving baseline, which integrates the community information through a matrix factorization; GNE [33]: a multi-layer community structure preserving baseline, which embeds communities onto surfaces of spheres; SpaceNE [36]: a method which applies subspace to the hierarchical network embedding model and preserves the proximity between pairwise nodes and between communities; we evaluate the performance of the proposed HGE-GA algorithm compared with the following baselines:
Summary
Fueled by the spectacular growth of the internet and Internet of Things, plenty of social networks such as Facebook, Twitter and WeChat have sprung up, changed the mode of interaction between people, and accelerated the development of viral marketing.Originally from the idea of word-of-mouth advertising, viral marketing takes advantage of trust among close social circles of friends, colleagues or families to promote a new product, i.e., when a friend relationship affects a user making decisions on item selection [1,2].Motivated by applications to early viral marketing, a new study area of influence diffusion has thrived. Fueled by the spectacular growth of the internet and Internet of Things, plenty of social networks such as Facebook, Twitter and WeChat have sprung up, changed the mode of interaction between people, and accelerated the development of viral marketing. From the idea of word-of-mouth advertising, viral marketing takes advantage of trust among close social circles of friends, colleagues or families to promote a new product, i.e., when a friend relationship affects a user making decisions on item selection [1,2]. Motivated by applications to early viral marketing, a new study area of influence diffusion has thrived. In terms of viral marketing, for example, if user v1 ,v2 ,v3 bought a product, their friends in a given social network will likely buy this product because of the friend-to-friend influence
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