DeepInf: Social Influence Prediction with Deep Learning (1807.05560v1)

Abstract: 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.

Analysis of Graph Convolutional Networks and Influence Propagation

The paper under examination presents a detailed paper on the workings and implications of Graph Convolutional Networks (GCNs) in the context of influence propagation. This research is pivotal for understanding the interplay between graph-based neural network models and their application in network analysis, particularly in modeling and predicting influence spread over various types of networks.

The core objective of the paper is to delve into the effectiveness of GCNs when harnessed for influence propagation tasks. The authors rigorously dissect the network architecture of GCNs to analyze how graph structures can be leveraged for efficient information dissemination. By drawing upon well-established graph-theoretic principles, this paper outlines the significant parameters and conditions affecting the propagation dynamics facilitated by GCNs.

A significant contribution of the paper is the introduction of comprehensive experiments that quantify the performance of GCNs across multiple datasets and tasks. The results exhibit substantial improvements in predictive capacity compared to traditional methodologies. Through these empirical evaluations, the paper demonstrates that GCNs offer superior performance in capturing the nuances of network influence propagation, achieving notable accuracy margins in diverse network settings. Such results underscore the potential of GCNs to outperform other influence-maximization strategies.

The authors contend that incorporating GCNs into influence modeling frameworks significantly enhances both the breadth and depth of analysis possible, ultimately broadening the horizon for potential application areas. The propagation of influence, which is notoriously complex due to non-linear interactions and dynamic network boundaries, is effectively modeled within the GCN framework, showcasing robust adaptability to different network scales and types.

From a theoretical perspective, the paper provides insights into the versatility of GCNs in processing relational data. It underscores the capacity of these networks to generalize complex patterns of interaction through multi-layered learning. This opens avenues for future research focused on architectural advancements and optimization of GCNs to further refine their ability to model influence propagation in increasingly complex and larger-scale datasets.

In terms of practical implications, the outcomes of this research suggest a promising integration of GCNs in areas such as social network analysis, recommendation systems, and information diffusion modeling. The practical applicability of the findings denotes a shift towards more dynamic and data-driven approaches in handling influence-related challenges across industries. By harnessing the predictive power of GCNs, practitioners can anticipate and influence network behaviors in real-time.

Looking ahead, the paper sets the stage for further exploration into the scalability of GCNs and their application in real-world network scenarios. There is a clear opportunity for extension into adaptive models that can learn and respond to temporal evolutions in networks, further solidifying the role of GCNs in the repertoire of advanced AI tools.

In conclusion, this research offers a significant contribution to the field by enhancing the understanding of how GCNs can be employed to model influence propagation efficiently and accurately. The findings reinforce the promising outlook of graph-based models in capturing complex network interactions, paving the way for innovative applications and future investigations in artificial intelligence and network analytics.