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Deployed on AWS
The Network Influencer Algorithm analyzes network connection data and provides 10 key measures of influence for each actor in the network.
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.
Highlights
- Provides several measures for key influencer identification using state-of-the-art methods in network analysis. We cover the following classes of measures - 1. Influence; 2. Closeness; 3. Centrality ; 3. Knowledge broker; 4. Hub& Authority; and 5. Communities & Clusters
- We organize the measures of influence into the following categories – Influence – Connection of a node to other important nodes in the network. Centrality – Based on the number of links incident upon a node. Closeness – how tightly knit the nodes are in the network based. Brokerage – If nodes in the network act as a bridge between different groups of the network. Hub & Authority – Pointed links to many other pages which have high authority. Communities – Measures of the cluster belonginess of the nodes. Each node is tagged to a specific cluster it belongs to in the network
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Deployed on AWS
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
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:
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
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