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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Mphasis HyperGraf Influencer Analytics

By: Mphasis Latest Version: 2.3

The Network Influencer Algorithm analyzes network connection data and provides 10 key measures of influence for each actor in the network.

Product Overview

Influencer identification in a network is a component of HyperGraf. The underlying algorithms are driven by state-of-the-art network analysis and graph theory methods, e.g. page-rank, degree centralities, betweenness, clustering co-efficients. A social network consists of a set of socially relevant actors connected by one or more relations which can characterize communications between the actors. Identifying influential nodes in such a network enables organizations to optimize the spread of information in such networks. Mphasis HyperGraf is an Omni-channel customer 360 analytics solution.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input:

Following are the mandatory inputs for predictions made by the algorithm:

  • Vertex 1
  • Vertex 2
  • Here, Vertex 1 represents actors/nodes that communicate with actors/nodes in Vertex 2.
  • Supported content types: 'text/csv'

Output:

  • Supported content types: 'text/csv'

Invoking endpoint:

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept text/csv out.csv

Substitute the following parameters:

  • "endpoint-name" - name of the inference endpoint where the model is deployed
  • input.csv - input image to do the inference on
  • text/csv - MIME type of the given input file (above)
  • out.csv - filename where the inference results are written to

Resources:

Additional Resources

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Support Information

Mphasis HyperGraf Influencer Analytics

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

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