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| Title | Technical Perspective - No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server |
| Authors | Aidan Hogan |
| Publication date | July 2022 |
| Abstract |
The machine learning community has traditionally been proactive in developing techniques for diverse types of data, such as text, audio, images, videos, time series, and, of course, matrices, tensors, etc. "But what about graphs?" some of us graph enthusiasts may have asked ourselves, dejectedly, before transforming our beautiful graph into a brutalistic table of numbers that bore little resemblance to its parent, nor the phenomena it represented, but could at least be shovelled into the machine learning frameworks of the time. Thankfully those days are coming to an end. |
| Pages | 41 |
| Volume | 51 |
| Journal name | SIGMOD Record |
| Publisher | ACM Press (New York, NY, USA) |
| Reference URL |
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