Measuring scholarly impact: Methods and practice Link prediction with the linkpred tool

Transcription

1 Measuring scholarly impact: Methods and practice Link prediction with the linkpred tool Raf Guns University of Antwerp

2 If you want to follow along Download and install Anaconda Python from Download the example data from

3 A pair of scientists who have five mutual previous collaborators, for instance, are about twice as likely to collaborate as a pair with only two, and about 200 times as likely as a pair with none. (Newman, 2001; emphasis mine)

4 Agenda What is link prediction? (and why?) Example data The linkpred tool Link prediction in practice Conclusion

5 What is link prediction?

6 Networks

7 Networks in informetrics Citation Papers Journals Authors Patents Collaboration Authors Institutions Countries Co citation Bibliographic coupling Web links And so on

8 Definitions A network G = (V, E) consists of: A set of nodes or vertices V A set of links or edges E Each link connects two nodes from V Neighbourhood N(v) of node v: all nodes connected to v Node degree N(v) of v: number of connected nodes = number of items in set N(v)

9 Change in networks Most networks are not static, e.g. in collaboration network: New authors appear Old authors disappear New collaborations are initiated Previous collaborators stop collaborating

10 Change in networks Some changes are more plausible than others

11 Change in networks Different mechanisms have been identified Assortativity: similar nodes are more likely to connect Preferential attachment: well connected nodes attract more new connections Cf. cumulative advantage, Matthew effect

12 The link prediction question Liben Nowell and Kleinberg (2003, 2007): Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future?

13 Link prediction steps 1. Data gathering 2. Preprocessing 3. Prediction 4. Evaluation

14 Steps

15 Why link prediction? You want to know which links will appear in the future Recommendation Finding missing links Finding anomalous links (correct or incorrect) Evaluating network formation and evolution models

16 Our example data

17 Data Guns and Rousseau (2013) Collaboration between cities in Africa and South Asia Topic: malaria In three consecutive time periods Available as three Pajek network files:

18

19

20

21 The linkpred tool

22 About Cross platform (written in Python) Open source: BSD license Command line tool! Alternative: LPmade (

23 How and where to get linkpred 1. Install Anaconda Python: 2. Open command line window 3. Run command: > pip install 4. Wait until installation is finished

24 Basic usage > linkpred Should display brief usage instructions > linkpred help Displays more complete help output

25 Basic usage > linkpred training network file predictors predictor output output type Read the network in training network file, predict using predictor and give output of output type > linkpred training network file test networkfile predictors predictor output outputtype Read the network in training network file, compare with testnetwork file, predict using predictor and give output of output type

26 Link prediction in practice

27 Preprocessing Nodes may also appear and disappear Restrict to intersection of node sets of training and test network Only where test network is available Restrict by degree (default: only discard isolate nodes) Directed networks: notsupported Convert to undirected first

28 Prediction: choosing predictors Local AdamicAdar AssociationStrength CommonNeighbours Cosine DegreeProduct Jaccard MaxOverlap MinOverlap NMeasure Pearson ResourceAllocation Global GraphDistance Katz RootedPageRank SimRank Other Community Copy Random

29 Local predictors Tendency towards triadic closure Number of common neighbours is a simple but powerful predictor.

30 Local predictors Common neighbours Normalizations of common neighbours Jaccard coefficient, cosine measure Adamic/Adar (Adamic & Adar, 2003), 1 log

31 Weighted networks In weighted networks, links have weights (e.g. number of joint papers, number of citations ) Link weights : often ignored!! Most predictors in linkpred can use link weights General idea: higher link weight (e.g., more common papers), stronger connection

32 Global predictors Graph distance: lowest number of links needed to travel from a to b problem: small world phenomenon

33 Global predictors Katz (1953): : 1 if i and j are linked, 0 otherwise : number of walks with length k from i to j : parameter, probability of effectiveness of a single link Longer walks: lower effectiveness

34 Global predictors Rooted PageRank

35 Global predictors Rooted PageRank

36 Global predictors SimRank (Jeh & Widom, 2002) Objects that link to similar objects are similar themselves.,, Starting point: a node is maximally similar to itself: W(v, v) = 1

37 Demo Predict Save predictions to file import in e.g. Excel

38 Evaluation Step 4: How well does it work? How? compare to known good test network Four groups: Link Non link Predicted True positive False positive Not predicted False negative True negative

39 Evaluation Simply save results to text file: output cache evaluations Create chart: Recall precision ROC

40 Evaluation: recall precision Precision: fraction of correct predictions Recall: fraction of correctly predicted links

41 Evaluation: ROC True positive rate: fraction of correctly predicted links (= recall) False positive rate: Fraction of incorrectly predicted links

42 Profiles A simple way to save and reuse the configuration of a complex prediction run (options, predictors, parameters ) Usage example: > linkpred network file profile profile.yml Format: YAML, see

43 Example profile predictors: name: AdamicAdar displayname: Adamic/Adar name: GraphDistance displayname: Graph distance parameters: weight: weight name: SimRank displayname: SimRank (c=0.4) parameters: c: 0.4 name: SimRank displayname: SimRank (c=0.8) parameters: c: 0.8 output: cache predictions recall precision

44 Conclusion

45 About link prediction Link prediction is possible because link formation is not a purely random process Limitations: Unaware of social and other circumstantial factors Which predictor is best for a concrete situation? Trade off between prediction accuracy and non triviality

46 About linkpred Relatively simple but powerful Limitations: Not suitable for very large and/or dense networks Does not incorporate more complex setups like predictor combinations, machine learning etc. All results can be exported for analysis in other software (cache *) Open source: contributions welcome!