Similarity for heterogeneous networks


We propose a generalization of SimRank similarity measure for heterogeneous information networks. Each node is associated with a set of objects and there are different possible relations. This is encoded into the adjacency tensor, and the TensorSimRank equation is given as

$$s_{\alpha \beta} = \sum_{\gamma} w_{\alpha \beta \gamma} r_{\alpha \beta \gamma} s_{\alpha \beta} r_{\beta \alpha \gamma}, \quad s_{\alpha \alpha} = 1.$$

This equation generalizes the classical SimRank similarity measure. The simple iteration combined with low-rank approximation of \(S\) was proposed as a computational algorithm.
The model was tested both on synthetic datasets and a real Book-Crossing Dataset. The final network has the structure (Book, Author, Year, Publisher), and an example of a “closest book” request to the Psychic Sisters is given in the Table below.

|Psychic Sisters (Sweet Valley Twins and Friends, No 70) | |————————————————————————————| |The Love Potion (Sweet Valley Twins and Friends, No 72) | |The Curse of the Ruby Necklace (Sweet Valley Twins and Friends Super, No 5) | |She’s Not What She Seems (Sweet Valley High No. 92) | |Are We in Love? (Sweet Valley High, No 94) | |Don’t Go Home With John (Sweet Valley High No. 90) | |In Love With a Prince (Sweet Valley High, No 91) |


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01/12/2015 New paper in SIMAX


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