International Business Machines Corporation

Sheetal Srivastava

Mandar Mutalikdesai

Kartikeya Vats

Anagha M

Ashish Rao

Lavina Durgani

Ratul Sarkar

Sandipto Neogi

Pranjal Srivastava

Rahul Reddy Nimmakayala

Debasish Kanhar

Ojasvi Bhalerao

A method and related system determine a localness measure that represents accessibility of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving focus node (FN) information related to an FN of a graph representing the signed network, receiving neighbor node (NN) information related to a plurality of NNs in the signed network, and determining the localness measure based on the NN information and the FN information. The method and system also determine a harmony measure that represents importance of a node connectiveness within a signed network. The method comprises, with a computer processor, receiving source node (SN) information related to an SN of a graph representing the signed network, receiving destination node (DN) information related to a DN in the signed network, and determining the harmony measure based on the SN information and the DN information.

Global and local measures of centrality for signed and unsigned networks

One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.

Unsupervised contextual label propagation and scoring

Systems and methods are described for generating learning roadmaps from unstructured information. The systems and methods may provide for extracting a plurality of named entities from one or more corpora of information, constructing a graph based on the named entities, inducing a subgraph from the graph based on a target named entity, wherein the subgraph includes a subset of the named entities, ordering the subset of the named entities based on the subgraph, and generating a learning roadmap for the target named entity based on the ordering.

Learning roadmaps from unstructured text

A mixing pattern system for networks is provided. One or more nodes in a network are analyzed. Grouping the one or more nodes into one or more classes within the network. A computer device analyzes one or more transactions between the one or more nodes in the network that include nodes within similar or distinct classes of the one or more nodes. A computer device identifies one or more mixing patterns associated with one or more transactions between the one or more nodes.

Network mixing patterns

Sentiment analysis is targeted toward a specific subject of interest (or selected subjects) in a passage of natural language text. A dependency tree is generated for the passage, and subtrees are found that have sentiment polarities which contribute to the subject(s) of interest. A targeted sentiment score is computed for the subject(s) of interest based on sentiment expressed in those subtrees. Consecutively occurring nouns in the passage are collapsed into a noun phrase, as are possessives with ensuing nouns. The sentiment expressed in a given subtree can be modified using various linguistic heuristics. For example, sentiment polarity which is modified by a negation word may be inverted, sentiment polarity which is modified by an intensifying word may be increased, or sentiment polarity which is modified by a diluting word may be decreased.

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Bengaluru, India

Sheetal Srivastava