Spreading vectors for similarity searchAlexandre SablayrollesMatthijs DouzeCordelia SchmidHervé JégouInternational Conference on Learning Representations
Given a networkG=(N,E), static network embedding aims to learn a low-dimensional representation for each nodei∈N. The node embedding matrix for all the nodes is given byU∈Rd×N, wheredis the dimension of the embedding vectors (d<N). Thei-th column ofU, i.e.,ui→∈Rd×1, repre...
1. Designing an effective strategy for therecommendationof new links to single users and verifying its effectiveness in an interest-based network. As opposed to the task of link prediction, link recommendation is a widely unexplored task and it has been addressed only for general-purpose networks....
Before a similarity on the final activation vectors is defined it needs to be considered that nodes with very high degrees will be activated to a higher level. They are more likely to be reached even if they are not located in the same dense region as the node from which activation has ...
First, we computed row sum vectors 𝐴𝑆AS and column sum vectors 𝐴𝑅AR of the probability matrix M for each column and row, respectively. Then, we sorted the array 𝐴𝑆AS by setting pointers from 1 to N in the array 𝐼𝑆IS and sorted the array 𝐴𝑅AR by setting ...
[32] focused instead on link detection using a classifier trained on the feature vectors that describe the nodes of the graph. Combining structural graph similarity measures and simple node-based features in a supervised learning approach to link prediction has been also tried in the past [9, 33...
, Ω · R ]∈ ℂ M t × R as the matrix collecting the code vectors of the R data streams. The space-domain spread signal is defined by the third-order tensor S ̄ ∈ ℂ M t × N × R , the (m t ,n,r)th element of which is given by s ̄ m t , n , r =...