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Gaze takes the list of edges of this network, and performs the following computation: "find pairs of people who share a lot of friends in common". The resulting network is made of people who share many friends, even if they are not necessarily directly friends one with each other. Features Gaze accepts directed and undirected networks. undirected networks Exactly the case of the network of Facebook friends above. directed networks Imagine a list of people and the pets they have (the same owner can appear on different lines if owning several pets) Paul,dog Paul, hamster Paul,cat Gerald,cat Gerald,dog Marie,horse Donald,Squirrel Donald,cat ... In this case, it is interesting to get a network made of Paul, Gerald, Marie and Donald (sources nodes), showing how Similar they are in terms of pets they own. Make sure you do this by choosing "directed networks" in the parameters of Gaze. A related option for directed networks: you can choose a minimum number of times Paul should appear as a source to be included in the computations (useful to filter out unfrequent, irrelevant nodes: because you want only owners with many pets to appear for instance). Another case of directed networks: Imagine a network of scientific journals citing other journals: Nature, Science (meaning: Nature is citing Science) Journal of Economics, Journal of Finance Journal of Finance, Econometrica Econometrica,Review of Economic Studies Review of Economic Studies, Science Review of Economic Studies, Journal of Economics... Again here, you are interested in the source nodes (the citing journals, and how similar they are in terms of the journals they cite). The option "minimum nb of times a node should appear in targets" lets you specify how often a journal should be CITED in order to appear in the final network. In the example above, with this parameter set to zero, the resulting network will include all the sources: Nature,Journal of Economics, Journal of Finance, Econometrica, Review of Economi

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