Synthetic disjoint and overlapping dynamic attributed networks with ground-truth information

Synthetic network 3: Ten timestamps with growing/shrinking communities (three groups of 80, 90 and, 100 nodes), via a degree-corrected SBM for the network structure. The proposed attributes assess strongly assortative (Dataset 1), weakly assortative (Dataset 2), and disassortative (Dataset 3) structures. 4. Overlapping synthetic networks: Using Greene's benchmark, we create 11 overlapping datasets with 10 snapshots.

Each network has 250 nodes, overlapping nodes from 0 to 50, and overlapping memberships from 2 to 25. Specific kinds of changes in communities of a dynamic network were simulated: - Birth and death: Two birth events per time step and two death events per time step. Dataset 1 has no overlapping nodes; Dataset 2 has five overlapping nodes and two overlapping memberships; Dataset 3 has ten overlapping nodes and two overlapping memberships; Dataset 4 has 25 overlapping nodes and two overlapping memberships; Dataset 5 has 50 overlapping nodes and two overlapping memberships; Dataset 6 has five overlapping nodes and five overlapping memberships; Dataset 7 has five overlapping nodes and ten overlapping memberships; Dataset 8 has five overlapping nodes and 25 overlapping memberships. - Merge and split: Two merge events per time step and two split events per time step.

Dataset 9 has five overlapping nodes and two overlapping memberships. - Expansion and contraction: Two expansion events per time step and two contraction events per time step, with a rate of 0.1. Dataset 10 has five overlapping nodes and two overlapping memberships. - Node switching between communities: A probability of 0.2 for a node to switch its community membership. Dataset 11 has five overlapping nodes and two overlapping memberships. 5.

Real-world network: The crime network comprises individuals who committed crimes in Chile between 2019 and 2021. The edges of the graph denote joint participation in a case. To construct the timesteps, the date of the crime was used to establish three-month intervals, where each period is non-overlapping.

The attributes considered in this dataset are the crimes committed by each subject, where each column designates a specific crime and a value of 1 indicates that the individual committed the corresponding crime. Robbery in an uninhabited place, robbery with violence, and homicide in a fight, fall under the scope of this network, which includes a total of 26 crime types. In this dataset, the attribute values are time-varying.