Closeness Centrality

Supported Graph Characteristics
 Unweighted edges Directed edges Undirected edges Homogeneous vertex types Heterogeneous vertex types

Algorithm link: Closeness Centrality

We all have an intuitive understanding when we say a home, an office, or a store is "centrally located." Something that is centrally located is roughly equidistant from several destinations.

Closeness Centrality provides a precise measure of how "centrally located" all the vertices are. The steps below show the steps for one vertex `v`:

Step Mathematical Formula

1. Compute the average distance from vertex v to every other vertex:

$d_{avg}(v) = \sum_{u \ne v} dist(v,u)/(n-1)$

2. Invert the average distance, so we have average closeness of v:

$CC(v) = 1/d_{avg}(v)$

This is repeated across all vertices in the graph.

Notes

This algorithm query employs a subquery called `cc_subquery`. Both queries are needed to run the algorithm.

The `re_type` (reverse edge type) parameter is always required. For undirected edges, use the same value for both `e_type` and `re_type`.

References

TigerGraph’s closeness centrality algorithm uses multi-source breadth-first search (MS-BFS) to traverse the graph and calculate the sum of a vertex’s distance to every other vertex in the graph, which vastly improves the performance of the algorithm.

The algorithm’s implementation of MS-BFS is based on the paper The More the Merrier: Efficient Multi-source Graph Traversal by Then et al.

Specifications

``````tg_closeness_cent (SET<STRING> v_type, SET<STRING> e_type, INT max_hops=10,
INT top_k=100, BOOL wf = TRUE, BOOL print_accum = True, STRING result_attr = "",
STRING file_path = "", BOOL display_edges = FALSE)``````

Parameters

Parameter Description Default

`SET<STRING> v_type`

Vertex types to use

(empty set of strings)

`SET<STRING> e_type`

Edge types to use

(empty set of strings)

`SET<STRING> re_type`

Reverse edge types to use

(empty set of strings)

`INT max_hops`

If >=0, look only this far from each vertex

10

`INT top_k`

Output only this many scores (scores are always sorted highest to lowest)

100

`BOOL wf`

Whether to use Wasserman-Faust normalization for multi-component graphs

True

`BOOL print_accum`

If True, output JSON to standard output

True

`STRING result_attr`

If not empty, store centrality values in `FLOAT` format to this vertex attribute

(empty string)

`STRING file_path`

If not empty, write output to this file in CSV format.

(empty string)

`BOOL display_edges`

If true, include the graph’s edges in the JSON output so that the full graph can be displayed.

False

Output

Computes a Closeness Centrality value (FLOAT type) for each vertex, calculated from the average distance between that vertex and every other vertex.

Result size

$V$, or the number of vertices. One FLOAT value is returned for each vertex.

Time complexity

This algorithm has a time complexity of $O(E)$ where $E$ is the number of edges.

Parallel processing reduces the time needed for computation.

Example

Closeness centrality can be measured for either directed edges (from `v` to others) or for undirected edges. Directed graphs may seem less intuitive, however, because if the distance from Alex to Bob is 1, it does not mean the distance from Bob to Alex is also 1.

For our example, we wanted to use the topology of a friendship graph, but to have undirected edges. We emulated an undirected graph by using both `Friend` and `Also_Friend` (reverse-direction) edges.

``````# Use _ for default values
RUN QUERY tg_closeness_cent(["Person"], ["Friend", "Also_Friend"], _, _,
_, _, _, _, _)``````