# Academic Network Representation via Prediction-Sampling Incorporated Tensor Factorization

arXiv: https://arxiv.org/abs/2504.08323 · HTML: https://mlsanity.com/paper/2504.08323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 89 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources](https://mlsanity.com/paper/2011.14867/) (2020)
- [Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm](https://mlsanity.com/paper/1804.03728/) (2018)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [Hypergraph Contrastive Collaborative Filtering](https://mlsanity.com/paper/2204.12200/) (2022)
- [Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating](https://mlsanity.com/paper/2211.14065/) (2022)
- [HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer](https://mlsanity.com/paper/2302.11329/) (2023)
- [Self-Supervised Hypergraph Transformer for Recommender Systems](https://mlsanity.com/paper/2207.14338/) (2022)
- [DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors](https://mlsanity.com/paper/2204.03145/) (2022)
- [MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering](https://mlsanity.com/paper/2204.02338/) (2022)
- [Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2304.12228/) (2023)
- [Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks](https://mlsanity.com/paper/2112.13078/) (2021)
- [QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks](https://mlsanity.com/paper/1810.08382/) (2018)
- [BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network](https://mlsanity.com/paper/1912.08925/) (2019)
- [An Adaptive Latent Factorization of Tensors Model for Embedding Dynamic Communication Network](https://mlsanity.com/paper/2408.16573/) (2024)

## Abstract (arXiv)

Accurate representation to an academic network is of great significance to academic relationship mining like predicting scientific impact. A Latent Factorization of Tensors (LFT) model is one of the most effective models for learning the representation of a target network. However, an academic network is often High-Dimensional and Incomplete (HDI) because the relationships among numerous network entities are impossible to be fully explored, making it difficult for an LFT model to learn accurate representation of the academic network. To address this issue, this paper proposes a Prediction-sampling-based Latent Factorization of Tensors (PLFT) model with two ideas: 1) constructing a cascade LFT architecture to enhance model representation learning ability via learning academic network hierarchical features, and 2) introducing a nonlinear activation-incorporated predicting-sampling strategy to more accurately learn the network representation via generating new academic network data layer by layer. Experimental results from the three real-world academic network datasets show that the PLFT model outperforms existing models when predicting the unexplored relationships among network entities.