Visual analysis framework for understanding missing links in bipartite networks
Example implementations described herein involve an interface for calculating and displaying missing links for data represented as a bipartite network, along with novel methods for improving link prediction algorithms in the related art. Through example implementations described herein, the accuracy of link prediction algorithms can be improved upon, thereby providing the user with a more accurate understanding of the data in the bipartite network.
1. A method, comprising:
for data represented as a bipartite network and for a set of missing links in the bipartite network:
calculating a weight for each of the missing links in the set based on bicliques of the bipartite network;
executing a link prediction algorithm configured to incorporate the weight for each of the missing links; and
providing ones from the set of missing links selected by the link prediction algorithm as predicted missing links of the bipartite network, wherein the providing by the link prediction algorithm comprises providing a score for each predicted missing link that indicates a probability that a respective predicted missing links exists between respective nodes, and
presenting the bipartite network as a bi-adjacency matrix comprising rows that represent a first type of node in the bipartite network, and columns that represent rows of a second type of node in the bipartite network, each of the entries in the matrix representing one link between a node of the first type and a node of the second type,
wherein the providing ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network comprises providing, by the link prediction algorithm, a score for each of the predicted missing links that indicates a probability that a respective predicted missing link exists between a respective node of the first type and a respective node of the second type, and
wherein the presenting the bipartite network comprises providing an interface configured to represent each of the entries in the matrix as a color hue according to the score, and configured to order the rows and columns of the bi-adjacency matrix according to a selected criteria.
2. The method of claim 1 , wherein the calculating the weight for the each of the missing links in the set based on bicliques of the bipartite network comprises:
for each pair of bicliques having a score based on a number of overlapping nodes meeting a threshold and a size of the each pair of bicliques, calculating the weight for ones of the set of missing links between the pair of bicliques.
3. The method of claim 2 , wherein the calculating the weight for ones of the set of missing links between the each pair of bicliques is based on the number of overlapping nodes and the size of the each pair of bicliques.
4. The method of claim 1 , wherein providing the ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network comprises presenting the predicted missing links linearly by probability.
5. A non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising:
for data represented as a bipartite network and for a set of missing links in the bipartite network:
calculating a weight for each of the missing links in the set based on bicliques of the bipartite network;
executing a link prediction algorithm configured to incorporate the weight for each of the missing links; and
providing ones from the set of missing links selected by the link prediction algorithm as predicted missing links of the bipartite network, wherein the providing by the link prediction algorithm comprises providing a score for each predicted missing link that indicates a probability that a respective predicted missing links exists between respective nodes, and
presenting the bipartite network as a bi-adjacency matrix comprising rows that represent a first type of node in the bipartite network, and columns that represent rows of a second type of node in the bipartite network, each of the entries in the matrix representing one link between a node of the first type and a node of the second type,
wherein the providing ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network comprises providing, by the link prediction algorithm, a score for each of the predicted missing links that indicates a probability that a respective predicted missing link exists between a respective node of the first type and a respective node of the second type, and
wherein the presenting the bipartite network comprises providing an interface configured to represent each of the entries in the matrix as a color hue according to the score, and configured to order the rows and columns of the bi-adjacency matrix according to a selected criteria.
6. The non-transitory computer readable medium of claim 5 , wherein the calculating the weight for the each of the missing links in the set based on bicliques of the bipartite network comprises:
for each pair of bicliques having a score based on a number of overlapping nodes meeting a threshold and a size of the each pair of bicliques, calculating the weight for ones of the set of missing links between the pair of bicliques.
7. The non-transitory computer readable medium of claim 6 , wherein the calculating the weight for ones of the set of missing links between the each pair of bicliques is based on the number of overlapping nodes and the size of the each pair of bicliques.
8. The non-transitory computer readable medium of claim 5 , wherein providing the ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network comprises presenting the predicted missing links linearly by probability.
9. An apparatus, comprising:
a processor configured to:
for data represented as a bipartite network and for a set of missing links in the bipartite network:
calculate a weight for each of the missing links in the set based on bicliques of the bipartite network;
execute a link prediction algorithm configured to incorporate the weight for each of the missing links; and
provide ones from the set of missing links selected by the link prediction algorithm as predicted missing links of the bipartite network, wherein the providing by the link prediction algorithm comprises providing a score for each predicted missing link that indicates a probability that a respective predicted missing links exists between respective nodes, and
present the bipartite network as a bi-adjacency matrix comprising rows that represent a first type of node in the bipartite network, and columns that represent rows of a second type of node in the bipartite network, each of the entries in the matrix representing one link between a node of the first type and a node of the second type,
wherein the providing ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network comprises providing, by the link prediction algorithm, a score for each of the predicted missing links that indicates a probability that a respective predicted missing link exists between a respective node of the first type and a respective node of the second type, and
wherein the presenting the bipartite network comprises providing an interface configured to represent each of the entries in the matrix as a color hue according to the score, and configured to order the rows and columns of the bi-adjacency matrix according to a selected criteria.
10. The apparatus of claim 9 , wherein the processor is configured to calculate the weight for the each of the missing links in the set based on bicliques of the bipartite network by:
for each pair of bicliques having a score based on a number of overlapping nodes meeting a threshold and a size of the each pair of bicliques, calculating the weight for ones of the set of missing links between the pair of bicliques.
11. The apparatus of claim 10 , wherein the processor is configured to calculate the weight for ones of the set of missing links between the each pair of bicliques is based on the number of overlapping nodes and the size of the each pair of bicliques.
12. The apparatus of claim 9 , wherein the processor is configured to provide the ones from the set of missing links selected by the link prediction algorithm as the predicted missing links of the bipartite network by presenting the predicted missing links linearly by probability.
13. The apparatus of claim 9 , wherein the processor is configured to:
in response to a selection on an interface of one of the predicted missing links:
conducting at least one of motif analysis or metric analysis on the selected one of the predicted missing links through adding the one of the predicted missing links in the bipartite network; and
providing a result of the at least one of the motif analysis or the metric analysis for the selected one of the predicted missing links.
14. The method of claim 1 , wherein each of the entries in the matrix for the missing links are represented according to a first color scale, wherein the interface is configured to represent existing links between a node of the first type and a node of the second type according a second color scale different than the first color scale.
15. The method of claim 1 , wherein the interface is configured to reorder the rows and columns of the bi-adjacency matrix according to selected criteria, each selected criteria corresponding to a different order, wherein the selected criteria comprises one of a node label, an average prediction score, and a total number of missing links.
16. The method of claim 1 , wherein the interface is configured to add a missing link as an entity in response to an input identifying an entity of the matrix.