====== To do ======
===== CociteSeer =====
Tsung Teng Chen, David C Yen: CociteSeer: A system to visualize large
cocitation networks. The Electronic Library · August 2010, DOI: 10.1108/02640471011033602
Shrink groups of nodes of degree 2 (small degree) to get an overall structure.
===== Extreme Multilabel Classification =====
* ... and logistic regression for multilabel classification - ?Cheng - [[http://scholar.google.si/scholar_url?url=http://www-old.cs.uni-paderborn.de/fileadmin/Informatik/eim-i-is/PDFs/ml09draft.pdf&hl=en&sa=X&scisig=AAGBfm2L5LISMMRne9mQUHqxl3YTtC84Hg&nossl=1&oi=scholarr&ved=0ahUKEwjyrb_-yNLYAhVBniwKHf0XCqkQgAMIKCgBMAA|PDF]]
* … local embeddings for extreme multi-label classification - ?Bhatia - [[http://scholar.google.si/scholar_url?url=http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification.pdf&hl=en&sa=X&scisig=AAGBfm11lzK9VA7ROdwjVnFAlDz04ozJVg&nossl=1&oi=scholarr&ved=0ahUKEwjyrb_-yNLYAhVBniwKHf0XCqkQgAMIKSgCMAA|PDF]]
* http://manikvarma.org/downloads/XC/XMLRepository.html
* http://www.machinedlearnings.com/2015/04/extreme-multi-label-classification.html
* https://link.springer.com/chapter/10.1007/978-3-319-64283-3_21
* http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=71852©ownerid=5960
* http://riemenschneider.hayko.at/vision/dataset/task.php?did=316
* http://www.uni-obuda.hu/users/szakala/SMC%202016%20pendrive/2032_smc2016.pdf
* http://proceedings.mlr.press/v70/si17a.html
* [[https://www.polibits.gelbukh.com/2016_54/IN-DEDUCTIVE%20and%20DAG-Tree%20Approaches%20for%20Large-Scale%20Extreme%20Multi-label%20Hierarchical%20Text%20Classification.pdf|IN-DEDUCTIVE and DAG-Tree]]
* https://www-cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf
* https://sourceforge.net/p/meka/mailman/message/35906727/
* https://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=18291
===== Network multiplication with threshold =====
December 14, 2017
Skip small values. Introduce a threshold value. At the end correct values of existing links.
===== Leaders approach to clustering with RC =====
December 14, 2017
Dissimilarities are computed between leaders.
===== Corrected fractional bibliographic coupling =====
January 2018
Jaccard(A,B;k) = |A ∩ B| / max(k,|A ∪ B|)
Poglej:
http://gtna.cs.uni-kl.de/en/publikationen/PDFs/ASystematicApproachToTheOneModeProjectionOfBipartiteNetworks.pdf
===== Intensity of node activity in temporal networks =====
January 2018
u -> v : v(i) = u(i)/(t(i)-t(i-1))
===== Wasserstein and TQ =====
March 20, 2018
Can the Wasserstein distance be combined with TQ?
Interval -> Quantiles
===== Clauset ordering of journals =====
March 21, 2018
Compute JCi = JW * Ci * WJ . What is the condensation structure (of the main weak component)? Apply Clauset ordering to it.
JA = JW * WA. What is "the right" normalization of JAJ = JA * AJ ?
===== Symbolic data =====
March 31, 2018, Piran
Let S1 = ∑ xi and S2 = ∑ xi2. We define μ = S1/n and σ2 = ∑ (xi - μ)2 / n = S2 / n - μ2 .
Instead of μ or ( μ, σ ) it is better to use ( n, μ ) or ( n, μ, σ ) because we can compute also the representative of
a union of disjoint sets X and Y, X ∩ Y = ∅ :\\
nX∪Y = nX + nY\\
μX∪Y = (nX⋅μX + nY⋅μY) / nX∪Y \\
σX∪Y2 = S2;X∪Y / nX∪Y - μX∪Y2 \\
where: S2;X∪Y = S2;X + S2;Y and S2;X = nX⋅(σX2 + μX2)
https://en.wikipedia.org/wiki/Standard_deviation
Ward's clustering method has a criterion function P(**C**) = ∑C∈**C** σC2 .
===== Brain networks =====
Is there a fast criterion to decide that a node is isolated (of degree 0)?
===== Citation analysis =====
April 25, 2018
Extend bibliography from "Exploring the Limits of Complexity: A Survey of Empirical
Studies on Graph Visualisation" (review for Information Visualization).
August 20, 2018
* Use node weights in citation networks for analysis
* Does Kirchoff law hold for node weights?
* Standardization of any network: link s to all IN-nodes, link all OUT-nodes to t, add (t,s) arc. The standardized network is strongly connected. It can be periodic. A loop on (t,s) arc?
===== Bibliographic analysis =====
See {{notes:doc:1findr.pdf}}. Analysis of works using important "keywords" from text.
===== Small world =====
Moskva, May 27, 2018
Milgram paper. Inbreeding can lead also some levels back. Clustering coefficient considers only one level inbreeding.
===== Configuration model =====
Moskva, May 29, 2018
Chung-Lu method for large networks. Each row u (or column) has the same partial distribution indeg[v]/m. Select randomly in each row u outdeg[u] nodes following this distribution using an improved tabelaric method - merging a sequence of basic random numbers? If you get duplicates or loops skip them.
See [[https://scholar.harvard.edu/joelmiller/random-network-generation|Miller]], [[https://scholar.harvard.edu/files/joelmiller/files/fast_chung_lu_generation.pdf?m=1413848771|paper]], [[http://networkx.lanl.gov/archive/networkx-1.6/reference/generated/networkx.generators.degree_seq.expected_degree_graph.html|code]].
Can be extended to
https://mathinsight.org/generating_networks_second_order_motif_frequency ?
See regularity measures in Module 2 at http://www.pitt.edu/~kpele/tele2125_spring15.html#lectures
===== Markov chains =====
Moskva, June 2, 2018
Markov chains can be used to summarize sequences of events. For example sequence of letters in text -> transition matrix
* generate random sequence determined by a MC
* can we use MC to measure the conformance of a sequence to MC ? Would this work: assign to each sequence as its value the geometrical mean of MC transition probabilities? Small values - atypical sequence; 0 value - forbiden sequence.
* normalization of the value? multiply it by number of states n ?
[[https://stats.stackexchange.com/questions/247675/calculate-the-probability-for-a-sequence-generated-by-a-graph|StackExchange]]
===== Network measures =====
Moskva, June 4, 2018
Zhukov's slides: Lecture 5/slide 8: Harmonic centrality; Lecture 7/slide 12: Regularity measure !?
===== Topogical approach to networks =====
LJ, November 8, 2018
Try to extend the approach from
prof. dr. Robin Henderson, Newcastle University: Statistical Topology and the Random Interstellar Medium (8. november 2018 - 11:00, IBMI)
to networks. See photos.
The results are the same for all monotonically increasing transformed values?!
===== Genealogy of the field =====
LJ, November 11, 2018
How to produce the genealogy (Figure 1) from
[[https://www.researchgate.net/publication/328574199_Spatial_Decision_Support_Systems_Three_decades_on/references|Spatial Decision Support Systems: Three decades on]]
{{vlado:notes:pics:sdss.png}}
===== Dictionary networks =====
Moscow, December 6, 2018
Use n(Dic)*t(n(Dic)) or d(X,Y) = |X ∩ Y| / min(|X|, |Y|) to define a (dis)similarity and determine the islands.
===== Network spreadsheet =====
Moscow, December 6, 2018
Igraph node/link attributes can be also structured object. Develop computation engine for values and functions on nodes/linka.
===== Function form =====
LJ, Januray 5, 2019
Express the function from the [[https://tech.paulcz.net/blog/future-of-kubernetes-is-virtual-machines/|blog]] / [[https://commons.wikimedia.org/wiki/File:Gartner_Hype_Cycle.svg|picture]].
Lognormal + natural growth (max,min,lim,shrink)
===== Interesting =====
Brian Nguyen:
Multivariate Statistical Analysis with R: PCA & Friends making a Hotdog
https://bookdown.org/brian_nguyen0305/Multivariate_Statistical_Analysis_with_R/
===== Temporal groups =====
14. Jul 2022
A temporal group is formed around a "leader" (generator, attractor).
===== Specificity =====
28. Mar 2024
# citatitons (w) inside the topic bibliography / total # of citations (w) inside OpenAlex
===== TF-IDF =====
29. Mar 2024
topic bibliography vs. OpenAlex