PyTorch-BigGraph: a large-scale graph embedding system Lerer et al., SysML'19 We looked at graph neural networks earlier this year, which operate directly over a graph structure. Via graph autoencoders or other means, another approach is to learn embeddings for the nodes in the graph, and then use these embeddings as inputs into a (regular) neural … Continue reading PyTorch-BigGraph: a large-scale graph embedding system

# Tag: Graph

Graph processing systems and algorithms

# ExFaKT: a framework for explaining facts over knowledge graphs and text

ExFaKT: a framework for explaining facts over knowledge graphs and text Gad-Elrab et al., WSDM'19 Last week we took a look at Graph Neural Networks for learning with structured representations. Another kind of graph of interest for learning and inference is the knowledge graph. Knowledge Graphs (KGs) are large collections of factual triples of the … Continue reading ExFaKT: a framework for explaining facts over knowledge graphs and text

# Graph neural networks: a review of methods and applications

Graph neural networks: a review of methods and applications Zhou et al., arXiv 2019 It’s another graph neural networks survey paper today! Cue the obligatory bus joke. Clearly, this covers much of the same territory as we looked at earlier in the week, but when we’re lucky enough to get two surveys published in short … Continue reading Graph neural networks: a review of methods and applications

# GraphIt: A high-performance graph DSL

GraphIt: a high-performance graph DSL Zhang et al., OOPSLA'18 See also: http://graphit-lang.org/. The problem with finding the optimal algorithm and data structures for a given problem is that so often it depends. This is especially true when it comes to graph algorithms. It is difficult to implement high-performance graph algorithms. The performance bottlenecks of these … Continue reading GraphIt: A high-performance graph DSL

# ASAP: fast, approximate graph pattern mining at scale

ASAP: fast, approximate graph pattern mining at scale Iyer et al., OSDI'18 I have a real soft spot for approximate computations. In general, we waste a lot of resources on overly accurate analyses when understanding the trends and / or the neighbourhood is quite good enough (do you really need to know it’s 78.763895% vs … Continue reading ASAP: fast, approximate graph pattern mining at scale

# Pixie: a system for recommending 3+ billion items to 200+ million users in real-time

Pixie: a system for recommending 3+ billion items to 200+ million users in real-time Eksombatchai et al., WWW'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site, or from the WWW 2018 proceedings page). Pinterest is a visual … Continue reading Pixie: a system for recommending 3+ billion items to 200+ million users in real-time

# Struc2vec: learning node representations from structural identity

struc2vec: learning node representations from structural identity Ribeiro et al., KDD'17 This is a paper about identifying nodes in graphs that play a similar role based solely on the structure of the graph, for example computing the structural identity of individuals in social networks. That's nice and all that, but what I personally find most … Continue reading Struc2vec: learning node representations from structural identity