This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs respectively. In existing literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm for the optimization through backpropagation, which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.

In the current literature, these two tasks are usually independently studied while they are actually highly correlated.

We would like to thank each of the reviewers for reading our manuscript and providing very useful feedback. Below, we address the key points in detail. To calculate Eq. 6, it takes ELBO bound on each edge. Moreover, vGraph is efficient and scalable compared to classical community detection methods. We have updated the manuscript to make this more clear.

Neural Information Processing Systems http://nips.cc/

The idea of combining community detection and learning node representation is quite natural but apparently it is not well entrenched in the network analysis community. The reviewers feel that for this reason the paper would be a valuable contribution to the field.

I would like to thank the authors for their response. Originality ----------- * Although the literature in node representation learning and community detection is quite vast, this paper is positioned in a way that diverts from the norm. The method proposed is treating the two tasks as one, and jointly learns embeddings, rather than going through iterations of optimizations. Moreover, the authors proceed to compare against a big number of these methods in the experimental section. Still, this work is the first to apply and combine them for community detection and node representation learning.

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs respectively. In existing literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm for the optimization through backpropagation, which regularizes the community membership of neighboring nodes to be similar in the latent space.

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs respectively. In existing literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities.

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.