| Title |
Modelling and analysis of the dynamics of adaptive temporal–causal network models for evolving social interactions
|
|---|---|
| Published in |
Computational Social Networks, June 2017
|
| DOI | 10.1186/s40649-017-0039-1 |
| Pubmed ID | |
| Authors |
Jan Treur |
| Abstract |
Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes. Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time. It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction. In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena. |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 3 | 25% |
| Student > Master | 3 | 25% |
| Student > Doctoral Student | 1 | 8% |
| Lecturer > Senior Lecturer | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 3 | 25% |
| Computer Science | 2 | 17% |
| Nursing and Health Professions | 1 | 8% |
| Neuroscience | 1 | 8% |
| Engineering | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 August 2017.
All research outputs
#5,940,181
of 22,997,544 outputs
Outputs from Computational Social Networks
#10
of 40 outputs
Outputs of similar age
#94,804
of 317,371 outputs
Outputs of similar age from Computational Social Networks
#3
of 4 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 3.9. This one scored the same or higher as 30 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,371 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.