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Figure 1: In-degree distribution of the SCC graph These two experiments show that any naive method of recommending based only on the feature vectors is unlikely to result in a good performance, since the category and uploader are by far the most expressive features and do not give good results. Hence, our motivation to use graph infor- mation in addition to just the features will likely result in a better performance. In Section 4, we explore how to create easy to use features from the graph information using node2vec. Figure 2: Clustering Coefficient for individual categories 4 Vector Embeddings of Graph Nodes node2vec [GL16] allows us to learn fixed-length feature representations of nodes in a graph. It learns a mapping from the set of nodes to a lower dimensional real vector space Rd such that the likelihood of recovering the neighbors of a node from these vectors is maximized. Their key in- sight lies in the ability to define a objective function optimizing which gives us the vector represen- tations. They use a set of biased random-walks to generate the neighborhood of a node. These can be tuned using parameters which defines a notion of a neighborhood for our particular graph. Consider a walk that has just traversed (t, v) and is now at v, it transitions using a edge (v, x) with probability πvx = α(t, x)wvx where wvx is the edge weight and 4PDF Image | Recommending Related YouTube Videos
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