email_eu_core_temporal — Emails at a European research institution, temporal (2003-2005)

Description

Directed temporal networks of the emails exchanged between members of a large European research institution over 803 days, from the anonymized email records underlying the SNAP email-Eu-core network, distributed by SNAP as email-Eu-core-temporal (Paranjape, Benson and Leskovec). Messages to or from people outside the institution are not included. Nodes are members of the institution, and an edge $(u, v, t)$ means that member $u$ sent an email to member $v$ at time $t$; an email with several recipients gives one edge per recipient. Repeated emails between the same members are separate edges (a multigraph).

There are five networks:

all: the emails among all 986 members (the largest weakly connected component of the email-Eu-core network);
dept1, dept2, dept3, dept4: the emails among the members of four departments of the institution.

The node identifiers differ between the networks, and from those of the email-Eu-core network and of the entry email_eu_core (the same records, as a bipartite network of emails and members, with the departments of all members), so that the members cannot be matched across them.

The networks have the following properties:

(Node) member_id: the anonymized identifier of the member in the source data.
(Edge) time: the time the email was sent, in seconds since the beginning of the data (the absolute dates are not given).

Tags

Social Communication Multigraph Timestamps Unweighted

Added

2026-10-27

Citation

A. Paranjape, A. R. Benson, and J. Leskovec, "Motifs in Temporal Networks." Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM), 601-610 (2017). 10.1145/3018661.3018731 Sci-Hub

Upstream URL OK

https://snap.stanford.edu/data/email-Eu-core-temporal.html

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
all	986	332,334	337.05	1160.06	74.75	34.17	0.05	0.57	7	1.00	Directed	Unipartite	member_id	time	936 KiB	1.6 MiB	1.6 MiB	1.4 MiB
dept1	309	61,046	197.56	786.49	23.72	4.99	-0.17	0.58	5	0.33	Directed	Unipartite	member_id	time	153 KiB	268 KiB	262 KiB	249 KiB
dept2	162	46,772	288.72	999.31	20.55	2.75	0.29	0.80	4	0.28	Directed	Unipartite	member_id	time	136 KiB	233 KiB	227 KiB	203 KiB
dept3	89	12,216	137.26	284.09	28.01	4.67	-0.01	0.57	4	1.00	Directed	Unipartite	member_id	time	40 KiB	64 KiB	62 KiB	59 KiB
dept4	142	48,141	339.02	1095.83	19.41	3.61	-0.08	0.78	3	0.27	Directed	Unipartite	member_id	time	109 KiB	205 KiB	199 KiB	185 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.