Search Results for author:
Found 8 papers, 5 papers with code
On the Role of Edge Dependency in Graph Generative Models
Our evaluation, conducted on real-world datasets, focuses on assessing the output quality and overlap of our proposed models in comparison to other popular models.
Latent Random Steps as Relaxations of Max-Cut, Min-Cut, and More
However, graphs often also exhibit heterophilous structure, as exemplified by (nearly) bipartite and tripartite graphs, where most edges occur across the clusters.
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.
Simplified Graph Convolution with Heterophily
Like SGC, ASGC is not a deep model, and hence is fast, scalable, and interpretable; further, we can prove performance guarantees on natural synthetic data models.
On the Power of Edge Independent Graph Models
We prove that subject to a bounded overlap condition, which ensures that the model does not simply memorize a single graph, edge independent models are inherently limited in their ability to generate graphs with high triangle and other subgraph densities.
DeepWalking Backwards: From Embeddings Back to Graphs
Our findings are a step towards a more rigorous understanding of exactly what information embeddings encode about the input graph, and why this information is useful for learning tasks.
Node Embeddings and Exact Low-Rank Representations of Complex Networks
In this work we show that the results of Seshadhri et al. are intimately connected to the model they use rather than the low-dimensional structure of complex networks.
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
We study the objective in the limit as T goes to infinity, which allows us to simplify the expression of Qiu et al. We prove that this limiting objective corresponds to factoring a simple transformation of the pseudoinverse of the graph Laplacian, linking DeepWalk to extensive prior work in spectral graph embeddings.
