Week 1

Literature reviews, overview of the project.

Meeting

sketch

• the idea is to use the dynamic measures to see the graph

• for HD features or combined features, using a connected scatter plot seems interesting

• narrow down the design, and focus more on the problem itself rather than users

Graph Measures

Measure Level

• time: snapshot → period → lifetime

• component: local → subgraph → global

	local	subgraph	global
snapshot	single value	single value	single value
period	aggr, series, derived	aggr, series, derived	aggr, series, derived
lifetime	aggr, series, derived	aggr, series, derived	aggr, series, derived

"aggregated value" refers to the calculated metric value from an aggregated graph. "series value" means a value sequences for each snapshot during the period. And "derived value" is the derived value from series value, such as extremes, average value, deviance, trend, stability, fastest increase, etc.

Details

Global

• Static

  • count, ratio

    • number of nodes, active nodes, links, node pairs, connected components, motifs (triads, cliques...), clusters, activation, redundancy

    • clustering coefficient

  • extreme

    • diameter,

  • average

    • characteristic path length ( shortest path length), efficiency (1/shortest path)

  • others

    • modularity

• Dynamic

  • global volatility

Local

• Static

  • centrality: identify the most important vertex or link in a graph

    • degree, strength (sum of weight)

    • eccentricity (max d_ij)

    • closeness (average shortest path length)

    • betweenness

  • others

    • pagerank, redundancy

• Dynamic

  • volatility: sum presence variance of links to a node

  • activation: count of new connections

High-Dimensional Features (learned from data)

Deepwalk, Graph2Vec, SDNE, matrix factorization, persistent diagram, adjacent matrix