Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization

• URL: http://arxiv.org/abs/2010.01400v2
• Date: Wed, 22 Mar 2023 18:49:54 GMT
• Title: Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
• Authors: Maryam Ramezani, Aryan Ahadinia, Amirmohammad Ziaei, and Hamid R. Rabiee
• Abstract summary: In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning and low-dimensional latent factors. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.
• Score: 3.399624105745357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks does not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. To jointly discover omitted diffusion activities and hidden network structures, we develop a probabilistic generative model called "DiffStru." The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled with low-dimensional latent factors. Besides inferring unseen data, latent factors such as community detection may also aid in network classification problems. We tested different missing data scenarios on simulated independent cascades over LFR networks and real datasets, including Twitter and Memtracker. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.

Related papers

• Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding [2.654975444537834]
  Key question in many network studies is whether the observed correlations between units are primarily due to contagion or latent confounding. We propose network causal effect estimation strategies that provide unbiased and consistent estimates. We evaluate the effectiveness of our methods with synthetic data and the validity of our assumptions using real-world networks.
  arXiv  Detail & Related papers  (2024-11-02T22:12:44Z)
• Valid Bootstraps for Networks with Applications to Network Visualisation [0.0]
  Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made. We propose a principled, novel, distribution-free network bootstrap using k-nearest neighbour smoothing.
  arXiv  Detail & Related papers  (2024-10-28T10:22:22Z)
• Model-Based Inference and Experimental Design for Interference Using Partial Network Data [4.76518127830168]
  We present a framework for the estimation and inference of treatment effect adjustments using partial network data. We illustrate procedures to assign treatments using only partial network data. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi.
  arXiv  Detail & Related papers  (2024-06-17T17:27:18Z)
• ProtoVAE: Prototypical Networks for Unsupervised Disentanglement [1.6114012813668934]
  We introduce a novel deep generative VAE-based model, ProtoVAE, that leverages a deep metric learning Prototypical network trained using self-supervision. Our model is completely unsupervised and requires no priori knowledge of the dataset, including the number of factors. We evaluate our proposed model on the benchmark dSprites, 3DShapes, and MPI3D disentanglement datasets.
  arXiv  Detail & Related papers  (2023-05-16T01:29:26Z)
• Latent Network Embedding via Adversarial Auto-encoders [15.656374849760734]
  We propose a latent network embedding model based on adversarial graph auto-encoders. Under this framework, the problem of discovering latent structures is formulated as inferring the latent ties from partial observations.
  arXiv  Detail & Related papers  (2021-09-30T16:49:46Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• On Robustness and Transferability of Convolutional Neural Networks [147.71743081671508]
  Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. We study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness.
  arXiv  Detail & Related papers  (2020-07-16T18:39:04Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Deep Collaborative Embedding for information cascade prediction [58.90540495232209]
  We propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-01-18T13:32:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.