Learning Graph Representations with Embedding Propagation

نویسندگان

  • Alberto García-Durán
  • Mathias Niepert
چکیده

Label Representations • Let l ∈ Rd be the representation of label l, and f be a differentiable embedding function • For labels of label type i, we apply a learnable embedding function l = fi(l) • hi(v) is the embedding of label type i for vertex v: hi(v) = gi ({l | l ∈ labels of type i associated with vertex v}) • h̃i(v) is the reconstruction of the embedding of label type i for vertex v: h̃i(v) = g̃i ({l | l ∈ labels of type i associated with the neighbors of vertex v}) Node Representations

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Graph Clustering with Dynamic Embedding

Graph clustering (or community detection) has long drawn enormous aŠention from the research on web mining and information networks. Recent literature on this topic has reached a consensus that node contents and link structures should be integrated for reliable graph clustering, especially in an unsupervised setting. However, existing methods based on shallow models o‰en su‚er from content nois...

متن کامل

Detecting Overlapping Communities in Social Networks using Deep Learning

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

متن کامل

A Unified View of Graph-based Semi-Supervised Learning: Label Propagation, Graph-Cuts, and Embeddings

Recent years have seen a growing number of graph-based semisupervised learning methods. While the literature currently contains several of these methods, their relationships with one another and with other graph-based data analysis algorithms remain unclear. In this paper, we present a unified view of graph-based semi-supervised learning. Our framework unifies three important and seemingly unre...

متن کامل

Building Graph Representations of Deep Vector Embeddings

Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enables the use of traditional machine learning algorithms on top of them. In this short paper we propose the construction of a graph embedding space in...

متن کامل

Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation

Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs is how to design effective and fine-grained course concepts such that students with different backgrounds can grasp the essence of the course. In this paper, we conduct a systematic investigation of the problem of course concept extraction for MOOCs. We propose ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017