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
Do we need specialized graph databases? Benchmarking real-time social networking applications
Do we need specialized graph databases? Benchmarking real-time social networking applications Pacaci et al., GRADES'17 Today's paper comes from the GRADES workshop co-located with SIGMOD. The authors take an established graph data management system benchmark suite (LDBC) and run it across a variety of graph and relational stores. The findings make for very interesting reading, … Continue reading Do we need specialized graph databases? Benchmarking real-time social networking applications
Mosaic: processing a trillion-edge graph on a single machine
Mosaic: Processing a trillion-edge graph on a single machine Maass et al., EuroSys'17 Unless your graph is bigger than Facebook's, you can process it on a single machine. With the inception of the internet, large-scale graphs comprising web graphs or social networks have become common. For example, Facebook recently reported their largest social graph comprises … Continue reading Mosaic: processing a trillion-edge graph on a single machine
Dependency-driven analytics: a compass for uncharted data oceans
Dependency-driven analytics: a compass for uncharted data oceans Mavlyutov et al. CIDR 2017 Like yesterday's paper, today's paper considers what to do when you simply have too much data to be able to process it all. Forget data lakes, we're in data ocean territory now. This is a problem Microsoft faced with their large clusters … Continue reading Dependency-driven analytics: a compass for uncharted data oceans
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