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Structural Deep Network Embedding - SIGKDD

Structural deep Network Embedding Daixin Wang1 , Peng Cui1 , Wenwu Zhu1. Tsinghua National Laboratory for Information Science and Technology 1. Department of Computer Science and Technology, Tsinghua University. Beijing, China ABSTRACT ment targeting often needs to cluster the users into communities in Network Embedding is an important method to learn low-dimensional the social Network . Therefore, mining the information in the net- representations of vertexes in networks, aiming to capture and pre- work is very important. One of the fundamental problems is how serve the Network structure. Almost all the existing Network em- to learn useful Network representations [5]. An effective way is bedding methods adopt shallow models. However, since the under- to embed networks into a low-dimensional space, learn vec- lying Network structure is complex, shallow models cannot capture tor representations for each vertex, with the goal of reconstructing the highly non- linear Network structure, resulting in sub-optimal the Network in the learned Embedding space.

Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to p-

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Collaborative Knowledge Base Embedding for Recommender …

www.kdd.org

network embedding method, termed as TransR, to extract items’ structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items’ tex-

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Graph Convolutional Matrix Completion

www.kdd.org

The decoder model is a pairwise decoder Aˇ = д(Z), which takes pairs of node embeddings (zi,zj)and predicts entries Aˇ ... Graph Convolutional Matrix Completion KDD’18 Deep Learning Day, August 2018, London, UK.

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Fake News Detection on Social Media: A Data …

www.kdd.org

Fake News Detection on Social Media: A Data Mining Perspective Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang \, and Huan Liuy yComputer Science & Engineering, Arizona State University, Tempe, AZ, USA

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XGBoost: A Scalable Tree Boosting System

www.kdd.org

gradient tree boosting [10]1 is one technique that shines in many applications. Tree boosting has been shown to give state-of-the-art results on many standard classi cation benchmarks [16]. LambdaMART [5], a variant of tree boost-ing for ranking, achieves state-of-the-art result for ranking 1Gradient tree boosting is also known as gradient boosting

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Deep Crossing: Web-Scale Modeling without Manually …

www.kdd.org

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argue that explaining predictions is an important aspect in getting humans to trust and use machine learning e ectively, if the explanations are faithful and intelligible. The process of explaining individual predictions is illus-trated in Figure 1. It is clear that a doctor is much better positioned to make a decision with the help of a model if

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Fake News Detection on Social Media: A Data Mining …

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Fake News Detection on Social Media: A Data Mining Perspective Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang \, and Huan Liuy yComputer Science & Engineering, Arizona State University, Tempe, AZ, USA zCharles River Analytics, Cambridge, MA, USA \Computer Science & Engineering, Michigan State University, East Lansing, MI, USA

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Knowledge Graph Embedding: A Survey of Approaches and ...

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Sobolev spaces and embedding theorems - ICMC

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