Fitting blockmodels to data

Panning, William H.

doi:10.1016/0378-8733(82)90014-4

Cited by 26 publications

(11 citation statements)

References 13 publications

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“…Friedman and Rubin 1967;Panning 1982). Alternatively, since the focus is on 01, one can identify cohesive subgroups by assigning actors so as to maximize 01 .…”

Section: Clustering To Maximize 01mentioning

confidence: 99%

Identifying cohesive subgroups

Frank¹

1995

Social Networks

154

146

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“…Friedman and Rubin 1967;Panning 1982). Alternatively, since the focus is on 01, one can identify cohesive subgroups by assigning actors so as to maximize 01 .…”

Section: Clustering To Maximize 01mentioning

confidence: 99%

Identifying cohesive subgroups

Frank¹

1995

Social Networks

154

146

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“…To define PGNMs, we must first discuss the concept of block-models [44]- [46]. A block-model is a compact representation of a subgraph.…”

Section: Probabilistic Generalization Of Network Motifsmentioning

confidence: 99%

“…Fig 6 also shows a subgraph, G2, which is far from the proposed block model (d = 0.7538). Finding the best block-model to fit arbitrary connectivity data is a combinatorially complex problem [44,45,46], requiring exhaustive testing of different assignments of nodes to roles. However, an efficient algorithm to detect PGNMs can be formed based on the fact that small network motifs in biological networks aggregate to form network motif topological generalizations [22,47].…”

Section: Probabilistic Generalization Of Network Motifsmentioning

confidence: 99%

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Coarse-graining and self-dissimilarity of complex networks

et al. 2005

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Can complex engineered and biological networks be coarse-grained into smaller and more understandable versions in which each node represents an entire pattern in the original network? To address this, we define coarse-graining units (CGU) as connectivity patterns which can serve as the nodes of a coarse-grained network, and present algorithms to detect them. We use this approach to systematically reverse-engineer electronic circuits, forming understandable high-level maps from incomprehensible transistor wiring: first, a coarse-grained version in which each node is a gate made of several transistors is established. Then, the coarse-grained network is itself coarse-grained, resulting in a high-level blueprint in which each node is a circuit-module made of multiple gates. We apply our approach also to a mammalian protein-signaling network, to find a simplified coarse-grained network with three main signaling channels that correspond to cross-interacting MAP-kinase cascades. We find that both biological and electronic networks are 'self-dissimilar', with different network motifs found at each level. The present approach can be used to simplify a wide variety of directed and nondirected, natural and designed networks.

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“…Emellett pedig a folytatólagos értékeket meghatározó eljárások két algoritmusát: a faktorelemzésre építő (ún. Minres) algoritmust (Comrey, 1962) és a korrelációsat (Hubert és Schultz, 1976;Panning, 1982). Tanulmányunkban két algoritmus eredményeit közöljük, amelynek érzékeit az 13. táblázat és a 20. melléklet tartalmazza.…”

Section: Az öSszekapcsolódás éS a Csoportformációk Elemzéseunclassified

Hálózatos technológiával segített, kutatásalapú tanulásban részt vett tanulóközösségek interakciós és kapcsolathálói, valamint a közös vizsgálódás hatásai felsőoktatási kurzuskörnyezetben

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Fitting blockmodels to data

Cited by 26 publications

References 13 publications

Identifying cohesive subgroups

Identifying cohesive subgroups

Coarse-graining and self-dissimilarity of complex networks

Hálózatos technológiával segített, kutatásalapú tanulásban részt vett tanulóközösségek interakciós és kapcsolathálói, valamint a közös vizsgálódás hatásai felsőoktatási kurzuskörnyezetben

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