More Like this

1. Direct Observation of Node-to-Node Communication in Zeolitic Imidazolate Frameworks

   https://doi.org/10.1021/jacs.8b06727  

   Pattengale, Brian ; SantaLucia, Daniel J. ; Yang, Sizhuo ; Hu, Wenhui ; Liu, Cunming ; Zhang, Xiaoyi ; Berry, John F. ; Huang, Jier ( September 2018 , Journal of the American Chemical Society)
2. Node Proximity is All You Need: A Unified Framework for Proximity-Preserving and Structural Node and Graph Embedding

   Zhu, Jing ; Lu, Xingyu ; Heimann, Mark ; Koutra, Danai ( May 2021 , Proceedings of the 2021 SIAM International Conference on Data Mining (SDM))
3. Direct Observation of Node-to-Node Communication in Zeolitic Imidazolate Frameworks

   Brian, P. ; SANTALUCIA, D. J. ; Yang, S. ; Hu, W. ; Liu, C. ; Zhang, X. ; Berry, J. F. ; Huang, J. ( September 2018 , Journal of the American Chemical Society)
4. CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains

   https://doi.org/10.1109/Blockchain50366.2020.00014  

   Cao, Steven ; Kadhe, Swanand ; Ramchandran, Kannan ( November 2020 , 2020 IEEE International Conference on Blockchain (Blockchain))
5. Node, Motif and Subgraph: Leveraging Network Functional Blocks Through Structural Convolution

   https://doi.org/10.1109/ASONAM.2018.8508729  

   Yang, Carl ; Liu, Mengxiong ; Zheng, Vincent W. ; Han, Jiawei ( August 2018 , IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018)

   Networks or graphs provide a natural and generic way for modeling rich structured data. Recent research on graph analysis has been focused on representation learning, of which the goal is to encode the network structures into distributed embedding vectors, so as to enable various downstream applications through off-the-shelf machine learning. However, existing methods mostly focus on node-level embedding, which is insufficient for subgraph analysis. Moreover, their leverage of network structures through path sampling or neighborhood preserving is implicit and coarse. Network motifs allow graph analysis in a finer granularity, but existing methods based on motif matching are limited to enumeratedmore »simple motifs and do not leverage node labels and supervision. In this paper, we develop NEST, a novel hierarchical network embedding method combining motif filtering and convolutional neural networks. Motif-based filtering enables NEST to capture exact small structures within networks, and convolution over the filtered embedding allows it to fully explore complex substructures and their combinations. NEST can be trivially applied to any domain and provide insight into particular network functional blocks. Extensive experiments on protein function prediction, drug toxicity prediction and social network community identification have demonstrated its effectiveness and efficiency.« less