%2C%20and%20the%20cluster%20indices%20computed%20by%20our%20recursive%20spectral%20bisection%20(RSB)%20algorithm.%20These%20measures%20are%20flattened%20over%20the%20one%20dimensional%20vector%20space%20into%20their%20respective%20sub-component%20ranges%20such%20that%20the%20entire%20set%20of%20vector%20similarity%20functions%20could%20be%20used%20for%20finding%20similar%20nodes.%20The%20error%20is%20defined%20by%20the%20sum%20of%20pairwise%20square%20differences%20across%20a%20randomly%20selected%20sample%20of%20graph%20nodes%20between%20the%20assumed%20embeddings%20and%20the%20ground%20truth%20estimates%20as%20our%20novel%20loss%20function.%20The%20ground%20truth%20is%20estimated%20to%20be%20a%20combination%20of%20pairwise%20Jaccard%20similarity%20and%20the%20number%20of%20overlapping%20labels.%20Finally%2C%20we%20demonstrate%20a%20multi-variate%20stochastic%20gradient%20descent%20(SGD)%20algorithm%20to%20compute%20the%20weighing%20factors%20among%20sub-vector%20spaces%20to%20minimize%20the%20average%20error%20using%20a%20random%20sampling%20logic.)
![A knowledge graph algorithm enabled deep recommendation system [PeerJ]](https://dfzljdn9uc3pi.cloudfront.net/2024/cs-2010/1/fig-5-full.png)
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