WebInf2vec: latent representation model for social influence embedding. Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2024 2024 Conference paper DOI: 10.1109/ICDE.2024.00089 EID: 2-s2.0-85057118934. Contributors ... Web28 aug. 2024 · Online social networks are crowded with massive information, which is more likely to spread rapidly on a large scale. Therefore, understanding and predicting information diffusion on social networks will be much helpful to improve the performance of marketing and control the dissemination of misinformation.

Mathematics Free Full-Text A Node Embedding-Based …

WebI have applied the same in fields like biomedical, railways and Finance. Worked as a research intern at Max Planck Institute for Solar System Research, Germany. I have worked in collaboration with Indian Railways in detecting cracks in railway track for preventing accidents. Interested in various fields like Digital logic, deep learning ... WebReferences 1. K. Asghari, M. Masdari, F. S. Gharehchopogh and R. Saneifard , Multi-swarm and chaotic whale-particle swarm optimization algorithm with a selection method based on roulette wheel, Exp. Syst. 38 (2024) e12779. Google Scholar; 2. A. Bakhthemmat and M. Izadi , Communities detection for advertising by futuristic greedy method with clustering … ezstaff

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Web25 aug. 2015 · This paper proposes a relaxed learning process of the well-known Independent Cascade model that, rather than attempting to explain exact timestamps of users' infections, focus on infection probabilities knowing sets of previously infected users. Probabilistic cascade models consider information diffusion as an iterative process in … WebExperimental studies demonstrate the superiority of our proposed approach over the state-of-the-art algorithms in both next new POI recommendation and future visitor prediction. Second, we develop a new latent representation model Inf2vec to learn representations of users in a social network, such that the social influence is captured. Webinf2vec [7]. In this way, they combine the signals of diffusions and network links. However, they often only care about local network embedding that captures the diffusion structures rather than all links on the network, and they do not integrate node contents. Variational autoencoders Variational autoencoders (VAEs) [15, hilari mantel knjige