Skip to content

Latest commit

 

History

History
50 lines (40 loc) · 3.47 KB

README.md

File metadata and controls

50 lines (40 loc) · 3.47 KB
Raw

ONBRA Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks (Santoro and Sarpe, TheWebConf 2022)

How to build

  • mkdir build.build
  • cd build.build
  • cmake ../src
  • make

How to use

  • Build (see How to build above)
  • Use the ./onbra executable by supplying a graph in the proper format (described below)
  • Use ./onbra -h to see a list of available flags and options
  • Note: option -E selects if you need to compute the Temporal Betweenness for shortest paths (set it to 1) or for shortest $\delta$-restless walks (set it to 3 and in such case you will also need the parameter -D)

Example to use ONBRA for shortest paths with a sample size of 1000 pairs of nodes over 10 executions and appending all the results in "result.txt": ./onbra -f <filename> -d -s -E 1 -S 1000 -I 10 &> result.txt

Example to use ONBRA for restless walks with a sample size of 1000 pairs of nodes, delta 3200, over 10 executions and appending all the results in "result.txt": ./onbra -f <filename> -d -s -E 3 -S 1000 -I 10 -D 3200 &> result.txt

Output of ONBRA

A sample of the output that you may obtain by running ONBRA is the following (we comment each line by adding an arrow (→)):

Samples used: $S$ → $S$: is the sample size you provided in input.
Bound epsilon, max with $S$ samples is: $\varepsilon$ → $\varepsilon$: is a bound on the supremum deviation in current iteration using the empirical Bernstein bound (see paper).
Time to initizialize structures: $t_1$ → $t_1$: time needed to initialize internal structures to ONBRA
Time to compute forward paths: $t_2$ → $t_2$: time to compute paths for sampled pairs of nodes
Time to compute betweenness values: $t_3$ → $t_3$: time used to process identified paths to update node values
Paths to s-z found: $P$ → $P$: number of pairs of nodes $(s,z)$ sampled for which there exists at least a path from $s$ to $z$.
Time needed to read and run sampling alg: $t_{tot}$ → $t_{tot}$: total time to run an iteration of ONBRA (is at least $t_1+t_2+t_3$)
Node 0: $[b(0)_1' \cdots b(0)_I']$ -> each $b(0)_i'$ is the estimate obtained by ONBRA in $i$-th iteration ($i \in [1,I]$) for node 0
$\cdots$
Node $n-1$: $[b(n-1)_1' \cdots b(n-1)_I']$ -> each $b(n-1)_i'$ is the estimate obtained by ONBRA in $i$-th iteration ($i \in [1,I]$) for node $n-1$

Graph format for input into the ONBRA

Temporal graphs which are read by the benchmark suite need to have the following form:

  • A graph is represented by a sequence of lines, with each corresponding to a temporal edge in the graph
  • Node IDs should be non negative integers (starting from 0 included), such as 42 or 302 are valid node IDs.
  • Timestamps must be positive integers (strictly greater than 0) but such that they fit inside 64-bit signed integer
  • Each line of the input must start with the following description of an edge: ID of the origin (tail) node, ID of the destination (head) node, timestamp, all separated by (non-newline) whitespace.
  • We assume the input network is pre-processed such that edges appear time-ordered, and nodes appear sequentially, i.e., node id $i$ cannot appear on one edge before $i-1$ has not been seen on some other edge. We provide a script to preprocess a dataset in the folder utils.
  • Self-loops and duplicate edges are allowed, however they will be ignored Example of a valid temporal network:
    0 1 1
    1 2 1
    1 2 2
    2 3 3