{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n", "SPDX-License-Identifier: Apache-2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to utilize the GameTech Cohort Modeler\n", "\n", "This notebook shows how to use the GameTech Cohort Modeler. The code sample provided with the GameTech Cohort Modeler will deploy an Amazon Neptune database and a set of APIs that utilize Gremlin to construct a knowledge graph for a games customer based on structured and non-structed data from [AWS Labs](#).\n", " \n", " - [Background](#Background)\n", " - [Getting Started](#Getting-Started)\n", " - [Cohort Modeler Data Model](#Cohort-Modeler-Data-Model)\n", " - [Basic CRUD Operations](#Basic-CRUD-Operations)\n", " - [Traversing The Graph](#Traversing-the-Graph)\n", " - [Graph Based Predictive Queries](#Graph-Based-Predictive-Queries)\n", " - [Conclusion](#Conclusion)\n", " \n", "## Background\n", "\n", "The GameTech Cohort Modeler is based upon the concepts of modern knowledge graphs and are the result of connections of data from multiple different sources. These sources can either be multiple different databases, different data silos, or data extracted from within entities stored in one or more of these options. Knowledge graphs come in many different forms but the unifying aspect of them is that they organize data using the entities and connections (known as semantics) familiar to a particular domain. It represents these semantics as definitions of concepts, their properties, relations between them, and the expected logical constraints. Logic built into such a model allows us to infer understanding and connections about the information contained within the model.\n", "\n", "With the GameTech Cohort, we have aimed to consolidate and integrate player data, relationships between players, the activities they engage in, and the marketing campaigns they interact with. With the API set created within the GameTech Cohort Modeler we operationalize the ability to visualize and query on these relationships to answer with data that was previously not queryable. \n", "\n", "The examples in this use case show how we can use the Cohort Modeler to demonstrate how we can use the connected nature of our knowledge graph to provide contextually relevant answers to search questions." ] }, { "attachments": { "CohortModelerDiagram.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started\n", "\n", "The dataset used in this notebook was sythensized to recreate the needs of a mid-size game development studio that would be tracking metrics across ingame behaviors, player session length, and marketing campaign data. This dataset contains individual player profiles with a unique UUID to represent a player across multiple platforms, as well as marketing camapaigns that have engaged with the user. To make the data set more realistic, we have added additional features to this data using the [python faker](https://faker.readthedocs.io/en/master/) library and Numpy to simulate standard distributions. We have created a datamodel that will be flexible enough for customers to extend as needed. A graphical representation of the datamodel can be seen in the following image:\n", "\n", "![CohortModelerDiagram.png](attachment:CohortModelerDiagram.png)\n", "\n", "This data model could be used as the starting point for your own cohort model. In most cases, a studio will have data related to ingame actions, marketing campaigns, transactional data, session data, player technical data and other relevant business or telemetry data information. Users in this data model are represented with a UUID. There are some cases in which you may need to merge two users together as they are the same player playing on different platforms. We have assumed here that the data set provided has already gone through and merged all of the known like profiles together and we are storing relationships between users. \n", "\n", "***There is a process called [identity (or entity) resolution](https://en.wikipedia.org/wiki/Record_linkage#Entity_resolution) that you will need to go through to merge the two. The process of identity resolution can involve both deterministic patterns (such as matching users coming from the same public IP address within a certain time frame) or through more probabilistic means (using machine learning).***\n", "\n", "***If your studio currently does not capture some or all of the data you would like to see represented on the graph you can take a look at the [Serverless Analytics Solution](https://docs.aws.amazon.com/solutions/latest/game-analytics-pipeline/architecture.html).*** \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cohort Modeler Data Model\n", "\n", "**Vertexes** should contain *aggregate* data relevant to the vertex type\n", "\n", "**Player vertices** should be expected to contain aggregate behavioral data, including community engagement, financial engagement, marketing campaign engagement and so forth. They should also be expected to contain a unique GUID that allows a deeper dive into player data by following that GUID to a DynamoDB record which will expose all relevant data in detail, rather than in aggregate.\n", "\n", "**Activity vertices** should be expected to contain the metadata which defines how to alter the behavioral profile of a player who engages in the activity. For instance, if ‘spamming’ were to be rated at a +1 to mischief on a player profile, we’d note that on the SPAM Vertex.\n", "\n", "**Marketing vertices** should be expected to contain aggregate data about the campaign, updated on a regular basis. Data items like DAU/MAU and others (covered later on in the example data model) would be noted and updated from a Pinpoint analytics stream on a regular scheduled basis. Again, a GUID should be expected here as well, so specific detailed information pertaining to the campaign could be located in a DynamoDB record.\n", "\n", "**Edges** contain only the data describing *interactions* between two vertexes, and the direction of the edge should accurately indicate the originator and the target of the activity.\n", "\n", "**Edges between Players and Players**: Bidirectional edges between players should be expected, as interactions between players will never be symmetrical. In some cases, we’ll see unidirectional edges when one player refuses to engage with another, but usually there should be some indication as to why (Player A whispers Player B, and then Player B files a harassment report - we now have bidirectional edges between the two).\n", "\n", "**Edges between Players and Activities**: In most cases, unidirectional edges are to be expected since players engage in activities, but activities do not solicit players. That said, Bidirectional edges between players and activities could be useful when a game supports mechanisms to direct players to engage in those activities, like recommendation engines. \n", "\n", "**Edges between Players and Campaigns**: Bidirectional edges between players and marketing campaigns can be expected when those campaigns are targeted at explicit cohorts and not at the global player base. An edge out to a player from a campaign would include the number of times the targeted player was sent communications and via which channels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking to see if we have a connection to the Neptune Database\n", "The %status command will let you know the health of your Neptune Graph Database Cluster and the supporting library versions installed." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%status" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Checking what graph notebook version is currently running\n", "The %graph_notebook_version command will return the graph notebook version software running in this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%graph_notebook_version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading the Identity Graph\n", "\n", "To get started with the GameTech Cohort Modeler, we will load a set of data from the data model shown above into our Neptune cluster. Run the following two cells to download and load this data. It will take approximately 5-6 minutes to complete this next step. ***This will load approximately 12,000 player profiles, campaigns and activites with their corresponding data.*** \n", "\n", "Currently we are creating sample data within a second notebook titled \"CohortModelerDataGenerator\" which should have come with the code sample. Start there and upload the data set to S3 before you move on in this notebook.\n", "\n", "If using the data gen script you will want to load user_vertices.csv, campaign_vertices.csv, action_vertices.csv, interaction_edges.csv, engagement_edges.csv, campaign_edges.csv, and campaign_bidirectional_edges.csv in that order. If you run into an issue with the bulk loader, take a look at the [AWS documentation for trouble shooting](https://docs.aws.amazon.com/neptune/latest/userguide/load-api-reference.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%seed --model Property_Graph --dataset cohort_modeler --run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic CRUD Operations\n", "To start off with we will have four basic CRUD operations showcasing how to create a new vertex or edge." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Vertex\n", "Creating a player node is done by using the addV method and the value passed is the label for the new vertex. The properties that we need to add are an ID which should ascribe to a unique GUID id. You can add more values associated values you want to contain on the vertex within the property feild in a key value pair if needed.\n", "\n", "**To add different vertices for Activities and Campaigns you would replace the 'player' label with the label you want for the vertex.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.addV('player').property(id, 'newplayer1').next()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read the Values on a Target Vertex" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').elementMap().toList()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Updating Values on Vertices\n", "Vertices have values corresponding to a particular play which correspond to data that is either an emergent action or a statistic. Emergent actions such as altruism, malice, mischief and duty will all need to be incremented or decremented based upon a previous value. Gremlin has no increment feature and requires a unique query to read a value and increment from a previous value rather than overwrite the current value. This can be seen in the following query incrementing the value by 1." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').\n", " property(\n", " single,\n", " 'ae_altruism',\n", " union(values('ae_altruism'), constant(1)).sum()).\n", " valueMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a second player" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.addV('player').property(id, 'newplayer2').next()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating an Edge Between Two Vertices\n", "To create an edge between two vertices you will need to know the GUIDs of each vertex. When creating an edge you ahve the ability to set values on the edge based upon the action taken. In the case of the following command we are creating an edge with single chat value of 1." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').\n", " addE('interaction_edge').to(V('newplayer2')).\n", " property('chat', 1).\n", " next()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create an edge in the opposite direction\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer2').\n", " addE('interaction_edge').to(V('newplayer1')).\n", " property('chat', 1).\n", " next()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the Values on an Edge (Unidirectional)\n", "~~Edges in the Cohort Modeler can be bidirectional or unidirectional. To read a single edge moving out from a target vertex you would use the following query.~~" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').outE('interaction_edge').elementMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the Values on an Edge (Bidirectional)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').bothE('interaction_edge').elementMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the value between two targeted players" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').\n", " bothE('interaction_edge').\n", " where(otherV().hasId('newplayer2')).\n", " elementMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Updating a Value on an Edge\n", "Similar to vertices, edges have the ability to store values and a specific query is needed to increment a value from a previously stored value. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').\n", " outE('interaction_edge').\n", " where(inV().hasId('newplayer2')).\n", " property(\n", " 'action_sharepii',\n", " union(values('action_sharepii'), constant(1)).sum()).\n", " valueMap()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dropping an Edge Between Two Vertices\n", "To drop an edge between two vertices you will need to know the GUID ID of both vertices. The resulting command will drop all the values stored on the edge." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('newplayer1').bothE().where(otherV().is('newplayer2')).drop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Delete Vertex\n", "To drop a vertex. Place the ID of the vertex in the following command and run. When running this command this will also destroy the edges and the values interacting to and from the targeted vertex. All adjacent edges are dropped as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V(\"newplayer1\").drop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traversing the Graph\n", "Now that we have a number of basic queries for creating and reading values from vertices and edges. With the Cohort Modeler constructs we can query to find groupings of players who are interacting with each other and investigate unique relationships between players, activites, and campaigns to get a better understand how to group and view like player interactions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing First Order Relationships\n", "Find users that a given user's unique GUID has Interaction edges out. This query will return all the edges and the vertices that the edges are connected to and return back a graph visualization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%%gremlin -d ea_altruism\n", "g.V('237c54a8-20b6-46a2-aef8-0b2fb105d3cc').\n", " outE('interaction_edge').\n", " inV().\n", " path().by(elementMap())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing Second Order Relationships\n", "Find users that a given user's unique GUID has Interaction edges out and the edges of out of the players returned. This query will return all the edges and the vertices that the edges are connected to and return back a graph visualization." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin -p v,oute,inv,oute,inv\n", "g.V('237c54a8-20b6-46a2-aef8-0b2fb105d3cc').\n", " outE('interaction_edge').\n", " inV().\n", " outE('interaction_edge').\n", " inV().\n", " path().by(id()).by(label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing Third Order Relationships\n", "Find users that a given user's unique GUID has Interaction to the players that the players that the player queried interacted with. This query will return all the edges and the vertices that the edges are connected to and return back a graph visualization." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%%gremlin -p v,oute,inv,oute,inv,oute,inv -sd 50000\n", "g.V('237c54a8-20b6-46a2-aef8-0b2fb105d3cc').\n", " outE('interaction_edge').\n", " inV().\n", " outE('interaction_edge').\n", " inV().\n", " outE('interaction_edge').\n", " inV().\n", " path().by(id()).by(label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Graph Based Predictive Queries " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indirect related players (Triadic Closure)\n", "\n", "Find users that a given user has not directly interacted with, but that they might want to interact with based on common interactions. This query looks at players with similar player behavior to recommend people the player may want to interact with further." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin \n", "g.V('237c54a8-20b6-46a2-aef8-0b2fb105d3cc').as('user').out('interaction_edge').aggregate('friends').\n", " out('interaction_edge').\n", " where(neq('user')).where(without('friends')).\n", " groupCount().by(id()).order(local).by(values,desc).unfold()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Collaborative Triadic Closure Query\n", "\n", "\n", "Graph allows us to query based upon the emergent actions to find collaborative bad actors or find rings of bad actors. The players returned by this query may require additional monitoring resources from community based tools.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin\n", "g.V('237c54a8-20b6-46a2-aef8-0b2fb105d3cc').\n", " out('interaction_edge').\n", " where(\n", " out('action_edge').\n", " hasId('action_sharepii').\n", " in('action_edge').\n", " hasId('237c54a8-20b6-46a2-aef8-0b2fb105d3cc'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bad Actors Query\n", "\n", "This query will find players that have a reputation of 0 and return back all of the players that interacted with them. Similar to the previous query this query allows you to monitor how these players are interacting with the community at large and the players impact on other players." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%gremlin -d ea_reputation\n", "\n", "g.V().hasLabel('player').has('ea_reputation', lt(-75)).aggregate('starting_player').\n", " out('interaction_edge').\n", " where(\n", " outE('interaction_edge').\n", " has('action_sharepii').\n", " inV().\n", " in('interaction_edge').\n", " where(within('starting_player'))\n", " ).\n", " as('related_player').path().by(elementMap())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find all users related based on Interactions and Bad Actions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%gremlin -p v,oute,outv,oute,outv,ine,inv\n", "\n", "g.V().hasLabel('player').\n", " has('ea_reputation',lt(0)).as('starting_player').\n", " outE('interaction_edge').inV().outE('action_edge').inV().\n", " hasId('action_report','action_badimage','action_badlanguage','action_badname','action_sharepii').\n", " inE('action_edge').outV().\n", " //where(is(eq('starting_player'))). # come back to starting point\n", " path().by(id()).by(label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "The Cohort Modeler will be a key component in analyizing a games studious customer behaviors and providing deeper insights to microtarget future development, marketing campaigns, player behavior tools and other actions toward a subset of users that will maximize the value within a games community and future. In this notebook, we went through the data model, basic CRUD operations, graph traversals, and advanced graph queries to begin to have a deeper understanding of a games community and the basis potential use cases for future applications derived from the data contained in the Cohort Modeler. The examples contained in this notebook are just a small subset of how the Cohort Modeler can be utilized.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 4 }