Modelling Students’ Thematically Associated Knowledge: Networked Knowledge from Affinity Statistics

Koponen, Ismo T.

doi:10.1007/978-3-030-14459-3_10

Cited by 3 publications

(12 citation statements)

References 20 publications

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“…In the rewiring, the degree sequence of the nodes is preserved. We have shown that such affinity-based linking of nodes with minimal other assumptions reproduces the empirically discovered properties of associative knowledge networks quite adequately [26].…”

Section: Minimal Generative Model and Simulationsmentioning

confidence: 58%

“…We utilise here the generative model to produce a set of heavy-tailed networks that have a highly modular structure [26]. The model is based on generation of affinities for a fixed set of nodes, with the nodes assigned to different, pre-fixed classes.…”

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

“…The parameter λ determines the inverse power of the resulting degree distribution of the network, while Λ controls the cut-off of affinities, and π k < Λ(λ−1). The affinity distribution can be derived in several ways and by using several parametrisations, [26,30,31]. What is essential here is that the affinity distribution in Eq.…”

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

“…( 2) can be used to generate networks with the desired inverse power. Due to modularity, however, the resulting inverse power of degree distribution is never exactly λ but usually somewhat larger, λ > λ [26].…”

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

“…To test the applicability of q-adjacency kernels for networks that are heavytailed and modular, such as associative knowledge networks, we use a recently introduced generative model to produce networks that resemble the empirical networks [26]. The minimal model to generate the networks is introduced first, in section 2.…”

Section: Introductionmentioning

confidence: 99%

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Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels

Koponen,

Palmgren,

Keski-Vakkuri

2021

Preprint

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Heavy-tailed networks, which have degree distributions characterised by slower than exponentially bounded tails, are common in many different situations. Some interesting cases, where heavy tails are characterised by inverse powers λ in the range 1 < λ < 2, arise for associative knowledge networks, and semantic and linguistic networks. In these cases, the differences between the networks are often delicate, calling for robust methods to characterise the differences. Here, we introduce a method for comparing networks using a density matrix based on q-generalised adjacency matrix kernels. It is shown that comparison of networks can then be performed using the q-generalised Kullback-Leibler divergence. In addition, the q-generalised divergence can be interpreted as a q-generalised free energy, which enables the thermodynamic-like macroscopic description of the heavy-tailed networks. The viability of the q-generalised adjacency kernels and the thermodynamic-like description in characterisation of complex networks is demonstrated using a simulated set of networks, which are modular and heavy-tailed with a degree distribution of inverse power law in the range 1 < λ < 2.

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Section: Minimal Generative Model and Simulationsmentioning

confidence: 58%

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

Section: Minimal Generative Model and Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels

Koponen,

Palmgren,

Keski-Vakkuri

2021

Preprint

Self Cite

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Networked Knowledge and Complex Networks: An Engineering View

Lü

Wen

et al. 2022

IEEE/CAA J. Autom. Sinica

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Agent-Based Modeling of Consensus Group Formation with Complex Webs of Beliefs

Koponen

2022

Systems

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Formation of consensus groups with shared opinions or views is a common feature of human social life and also a well-known phenomenon in cases when views are complex, as in the case of the formation of scholarly disciplines. In such cases, shared views are not simple sets of opinions but rather complex webs of beliefs (WoBs). Here, we approach such consensus group formation through the agent-based model (ABM). Agents’ views are described as complex, extensive web-like structures resembling semantic networks, i.e., webs of beliefs. In the ABM introduced here, the agents’ interactions and participation in sharing their views are dependent on the similarity of the agents’ webs of beliefs; the greater the similarity, the more likely the interaction and sharing of elements of WoBs. In interactions, the WoBs are altered when agents seek consensus and consensus groups are formed. The consensus group formation depends on the agents’ sensitivity to the similarity of their WoBs. If their sensitivity is low, only one large and diffuse group is formed, while with high sensitivity, many separated and segregated consensus groups emerge. To conclude, we discuss how such results resemble the formation of disciplinary, scholarly consensus groups.

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Modelling Students’ Thematically Associated Knowledge: Networked Knowledge from Affinity Statistics

Cited by 3 publications

References 20 publications

Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels

Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels

Networked Knowledge and Complex Networks: An Engineering View

Agent-Based Modeling of Consensus Group Formation with Complex Webs of Beliefs

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