Similarity for heterogeneous networks
24/02/2015
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) |
