JeongchanKim

Traditional ML Pipeline All about designing proper features!! (hand-designed) Assume: nodes/links/graphs have some types of attributes associated with them, obtain features for all training data Goal: Train vectorized ML model (Random Foreset, SVM, NN) → Given a new node/link/graph obtaining its features and make a prediction by applying the model Design Choices: Features (d-dim), Objects: nodes..

Components of a Network Objects: nodes, vertices N Interactions: links, edges E System: network, graph G(N,E) Possible options: Weight, Ranking(최애, 차애), Type(친구, 동료), Sign(friend vs foe, trust vs distrust), Properties depending on the structures of the rest of the graph(Number of common friends) Directed vs Undirected Graphs Representing Graphs 1. Bipartite Graph A graph whose nodes can be divid..

Why is processing graphs hard? Network is complex (Arbitrary size and complex topological structure; no spatial locality like grids) No fixed node ordering or reference point Often dynamic and have a multi-modal feature Classic Graph ML Tasks Node classification: Predict a property of a node(Categorize online users/items) Link prediction: Predict whether there are missing links between two nodes..