Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights

@article{Yang2018MetaGraphBH,
  title={Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights},
  author={Carl Yang and Yichen Feng and Pan Li and Yu Shi and Jiawei Han},
  journal={2018 IEEE International Conference on Data Mining (ICDM)},
  year={2018},
  pages={657-666}
}
Heterogeneous information network (HIN) has drawn significant research attention recently, due to its power of modeling multi-typed multi-relational data and facilitating various downstream applications. [] Key Method Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with l2,1-loss, which finds the most informative meta-graphs and…

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References

SHOWING 1-10 OF 54 REFERENCES

Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

This paper re-examine similarity search in HINs and proposes a novel embedding-based framework, ESim, that accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space to explore network structure-embedded similarity.

Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks

This paper decomposes the original HIN schema into several semantically meaningful meta-graphs consisting of entity and relation types and presents a semi-supervised learning algorithm constrained by the types of HINs.

Discovering Meta-Paths in Large Heterogeneous Information Networks

This work proposes a greedy algorithm to select the most relevant meta-paths and presents a data structure to enable efficient execution of this algorithm and incorporates hierarchical relationships among node classes in their solutions.

Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

This paper introduces the concept of meta-graph to HIN-based recommendation, and solves the information fusion problem with a "matrix factorization + factorization machine (FM)" approach, and proposes to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta- graph based features.

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Two scalable representation learning models, namely metapath2vec and metapATH2vec++, are developed that are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, but also discern the structural and semantic correlations between diverse network objects.

HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning

Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8%, in link prediction.

PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks

A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN, and an inference algorithm is developed to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks.

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks

This work proposes a novel embedding learning framework-ASPEM-to preserve the semantic information in HINs based on multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

A Local Algorithm for Structure-Preserving Graph Cut

This paper focuses on mining user-specified high-order network structures and proposes a novel High-Order Structure-Preserving LOcal Cut (HOSPLOC) algorithm, which runs in polylogarithmic time with respect to the number of edges in the graph.

LINE: Large-scale Information Network Embedding

A novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted, and optimizes a carefully designed objective function that preserves both the local and global network structures.
...