Scalable attributed graph embedding for large-scale graph analytics

1. A computer-implemented method for performing graph analytics by learning attributed graph embeddings from node attributes and edge attributes of a graph, the method comprising:

computing a node embedding for a first node-attributed graph in a node embedded space;

generating one or more random attributed graphs in the node embedded space;

computing a graph embedding using a dissimilarity measure between one or more raw graphs and the one or more generated random graphs;

converting an edge-attributed graph into a second node-attributed graph using an adjoint graph;

computing the graph embedding for the second node-attributed graph obtained by the converting of the edge attributed graph;

fusing the computed node embedding from the second node-attributed graph and the first node-attributed graph into a graph representation.

2. The computer implemented method according to claim 1 , further comprising applying the graph representation to a machine learning (ML) or an artificial intelligence (AI) task-dependent analysis.

3. The computer implemented method according to claim 1 , wherein computing the node embedding is performed by one of an eigendecomposition, a deepwalk, a node2vc, or a LINE operation.

4. The computer implemented method according to claim 1 , wherein the generating random attributed graphs in the embedded space comprises sampling a plurality of sub-graphs from the first node-attributed graph.

5. The computer implemented method according to claim 1 , wherein the computing graph embedding for the second node-attributed graph by using a dissimilarity measure includes computing a graph feature matrix based on the special distance function by using random node-attributed graphs.

6. The computer implemented method according to claim 1 , wherein the converting an edge-attributed graph into the second node-attributed graph using an adjoint graph further comprises casting edge-attributed graphs as embedding node attributed graphs.

7. The computer implemented method according to claim 1 , wherein computing graph embedding for the second node-attributed graph includes passing a new node attributed graph.

8. The computer implemented method according to claim 1 , further comprising performing one or more of graph clustering, classification, or anomaly detection.

9. A system for performing graph analytics, comprising:

a processor configured to perform graph representation by learning attributed graph embeddings of node attributes and edge attributes of a graph;

a memory coupled to the processor, the memory storing instructions to cause the processor to perform acts comprising:

compute a node embedding for a first node-attributed graph in a node embedded space;

generate one or more random attributed graphs in the node embedded space;

compute a graph embedding using a dissimilarity measure between one or more raw graphs and the one or more generated random graphs;

convert an edge-attributed graph into a second node-attributed graph using an adjoint graph;

compute the graph embedding for the second node-attributed graph obtained by the converting of the edge attributed graph; and

fuse the computed node embedding from the second node-attributed graph and the first node-attributed graph into a graph representation.

10. The system according to claim 9 , wherein the processor is further configured to compute the node embedding for the first node-attributed graph by performing one of an eigendecomposition, a deepwalk, a node2vc, or a LINE operation.

11. The system according to claim 9 , wherein the processor is further configured to generate random attributed graphs in the embedded space by sampling a plurality of sub-graphs from the original node-attributed graph.

12. The system according to claim 9 , wherein the processor is further configured to compute the graph embedding for the second node-attributed graph by using a dissimilarity measure that includes computing a graph feature matrix based on the special distance function by using random node-attributed graphs.

13. The system according to claim 9 , wherein the processor is further configured to convert an edge-attributed graph into the second node-attributed graph using an adjoint graph by casting edge-attributed graphs as embedding node attributed graphs.

14. The system according to claim 9 , wherein the processor is further configured to compute graph embedding for the second node-attributed graph.

15. A non-transitory computer-readable storage medium tangibly embodying a computer-readable program code having computer-readable instructions that, when executed, causes a computer device to perform a method of graph analytics by learning attributed graph embeddings from node attributes and edge attributes of a graph, the method comprising:

computing a node embedding for a first node-attributed graph in a node embedded space;

generating one or more random attributed graphs in the node embedded space;

computing a graph embedding using a dissimilarity measure between one or more raw graphs and the generated random graphs;

converting an edge-attributed graph into a second node-attributed graph using an adjoint graph;

computing the graph embedding for the second node-attributed graph obtained by the converting of the edge attributed graph;

fusing the computed node embedding from the converted edge attributed graph and the first node attributed graph; and

providing the computed graph representations for a task-dependent analysis.

16. The computer-readable storage medium according to claim 15 , the method further comprising computing the graph embedding for the second node-attributed graph by using a dissimilarity measure that includes computing a graph feature matrix based on the special distance function by using random node-attributed graphs.

17. The computer-readable storage medium according to claim 15 , wherein the converting an edge-attributed graph into the second node-attributed graph using an adjoint graph further includes casting edge-attributed graphs as embedding node attributed graphs.