Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
By Maryam Ramezani, Aryan Ahadinia, Amirmohammad Ziaei Bideh, and Hamid R Rabiee.
Published in ACM Transactions on Knowledge Discovery from Data (TKDD).
🌐 ACM Digital Library: https://dl.acm.org/doi/abs/10.1145/3599237🌐 GitHub: https://github.com/maryram/DiffStru📢 Thrilled to unveil our latest research on #SocialNetworks! My paper dives into the challenges of missing data in large-scale networks from a novel point of view: partial observation of both the temporal cascades and the underlying structure. Introducing 'DiffStru,' a probabilistic generative model, we jointly uncover hidden diffusion activities and network structures through coupled matrix factorization. Excitingly, our approach not only fills gaps in data but also aids in network classification problems by learning coupled representations of temporal cascades and users. 🚀 Tested on synthetic and real datasets, the results are promising – detecting hidden behaviors and predicting links by unveiling latent features. 📊🔍
Our method uses the following input.
☝️ A partial observations of the underlying network as a graph: Nodes are representing users and directed links are corresponding to the following relations between users. All nodes are present but some links are omitted.
✌️ A partial sequential observation of user participations in information diffusion process, namely cascades: Users participate in cascades, e.g. retweeting a topic, in a social media. Our observation is a set of cascades with users participated in some of them in a specified timestamp.
The output of our method is as follows.
1️⃣ Predictions of omitted links in the underlying network.
2️⃣ Predictions of users' participations in cascades, including their timestamps.
3️⃣ A coupled representation of users and cascades which can be used for further analysis, e.g. community detection.