In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links.
Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online.
We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of ...
We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a hard problem of ...
Link Prediction in Social Networks Using Computationally Efficient ...
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Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online.
Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online.
Computationally efficient link prediction in a variety of social networks. Michael Fire, Lena Tenenboim-Chekina, Rami Puzis, Ofrit Lesser, Lior Rokach, Yuval ...
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The link prediction problem is related to finding unobservable links, missing links or near future interactions among members of a social network (Peng,. BaoWen ...
Computationally efficient link prediction in a variety of social networks. by Fire, Michael; Chekina, Lena Tenenboim; Puzis, Rami; Lesser, Ofrit; Rokach ...
Feb 1, 2024 · Link prediction is often used to speed up network data collection and impute connections missed by data gathering. As such, link prediction is a ...