Social Graph Mining Module


This module aims to provide dynamic machine learning capabilities to HELIOS users. In particular, each user (or, more precisely, their HELIOS device) carries a different instance of this module and needs to account for its capabilities to facilitate recommendation tasks. Recommendations are performed on a per-context basis and can facilitate various objectives.


This module depends on eu.h2020.helios_social.core.contextualegonetwork.

Jar File installation

This project can be downloaded as a jar file, which can be added on a Java project's dependencies. This requires also adding the Jar of the ContextualEgoNetwork library.

Gradle Installation

First, add the JitPack repository to your build file. In particular, add it in your root build.gradle at the end of repositories:

allprojects {
	repositories {
		maven { url '' }

Then add the dependency:

dependencies {
        implementation 'com.github.helios-h2020:h.extension-SocialGraphMining:1.0.4'

Maven Installation

First add the JitPack repository to your build pom file:


Then add the dependency:


API Usage

Here we detail how to develop applications using this module's graph API to provide social graph recommendation capabilities.

Instantiating the graph miner

Graph mining capabilities start from a contextual ego network instance. If such an instance is not available, it can be created when the application starts using the following code:

import eu.h2020.helios_social.core.contextualegonetwork.ContextualEgoNetwork;
String userid = "ego"; // can use any kind of user id for the ego node
String internalStoragePath = app.getApplicationContext().getDir("egonetwork", MODE_PRIVATE); // an internal storage path in Android devices
ContextualEgoNetwork contextualEgoNetwork = ContextualEgoNetwork.createOrLoad(internalStoragePath, userid, null);

The contextual ego network library is used by this module to attach information on the perceived social graph structure and can be saved using the command For a more detailed description see the documentation of the library. Only a singleton ContextualEgoNetwork instance should be created in an application and it should be shared with any other modules that potentially depend on it.

Given the contextual ego network instance, we can then set up a switchable miner to be able to alternate between different mining algorithms in runtime (e.g. to change the criteria interaction recommendations are sorted by). Here we will show how to alternate between two types of graph miners: one that re-recommends recent interactions (RepeatAndReplyMiner) and one that uses GNNs to recommend interactions (GNNMiner). Initializing a switchable miner and adding these two types of miners on it can be done with the following code:

import eu.h2020.helios_social.modules.socialgraphmining.SwitchableMiner;
import eu.h2020.helios_social.modules.socialgraphmining.heuristics.RepeatAndReplyMiner;
import eu.h2020.helios_social.modules.socialgraphmining.GNN.GNNMiner;

SwitchableMiner miner = new SwitchableMiner(contextualEgoNetwork);
miner.createMiner("repeat", RepeatAndReplyMiner.class);
miner.createMiner("gnn", GNNMiner.class).setDeniability(0.1, 0.1); //also apply 10% differential privacy and plausible deniability

Alternating between the above created miners can be done through their names through the commands miner.setActiveMiner("repeat"); and miner.setActiveMiner("gnn"); respectively. This alternation changes only which of the two miners is used for recommendation (see below) but simultaneously trains all of them whenever the switchable miner is notified of new interactions. In the above example we set the GNN miner as the type of miner the application starts recommending with.

It must be noted that, after instantiating a graph miner, such as the switchable miner, it needs to be constantly notified about user interactions and somes exchange parameters with other devices (see below).

Recommending interactions in the current context

Before explaining how to train the miners, it must be pointed out that predictions change as users switch contexts. In HELIOS, multiple contexts (e.g. home, work) may be defined by each user. In case where context switching is a not a necessity in an application, a generic-purpose context can be created and set up as the current one. Doing this with the contextual ego network management library can be achieved through the following code:


Then, using a social graph miner to obtain social recommendations for nodes of the contextual ego network in the current context can be achieved through the following code:

import eu.h2020.helios_social.core.contextualegonetwork.Context;
import eu.h2020.helios_social.core.contextualegonetwork.Node;

Context context = contextualEgoNetwork.getCurrentContext(); // can also obtain other contexts to recommend for
HashMap<Node, Double> interactWithNodeRecommendation = miner.recommendInteractions(context);

The recommendations are (Node, weight) entries for all nodes in the current context, where weight values lie in the range [0,1] with higher ones indicating stronger recommendation for interacting with the respective node.

Communication scheme

A requirement for using the social graph mining algorithms is that they need to exchange information when social interactions occur. Not doing so will considerably worsen the quality of some mining algorithms, especially those based on GNNs. Our design requires only little communication (i.e. three information exchanges) only when the interactions occur and of few parameters (e.g. at worst, expect a 100 double numbers converted to strings).

Our algorithms assume that, when the user of a device A initiates a social interaction towards the user of a device B the following communication steps are followed:

First step (SEND)

A creates an interaction in its instance of the contextual ego network retrieves a set of parameters (parametersOfA) from its miner given that interaction:

import eu.h2020.helios_social.core.contextualegonetwork.Interaction;

Interaction interaction = A.contextualEgoNetwork
				.getOrAddEdge(contextualEgoNetwork.getEgo(), contextualEgoNetwork.getOrCreateNode(nameOfB, null))
String parametersOfA = A.miner.getModelParameters(interaction);

Then A sends to B its parameters (e.g. by attaching them on the sent message or immediately after the sent message).

Second step (RECEIVE)

B receives the parameters of A (parametersOfA), creates a new interaction on its instance of the contextual ego network, notifies its miner about the receive and creates a new set of parameters (parametersOfB):

import eu.h2020.helios_social.core.contextualegonetwork.Interaction;
import eu.h2020.helios_social.modules.socialgraphmining.SocialGraphMiner.InteractionType;

Interaction interaction = B.contextualEgoNetwork
				.getOrAddEdge(contextualEgoNetwork.getEgo(), contextualEgoNetwork.getOrCreateNode(nameOfA, null))
B.miner.newInteraction(interaction, parametersOfA, InteractionType.RECEIVE);
String parametersOfB = miner.getModelParameters(interaction);

Then B sends back to A its parameters (e.g. by attaching to a receive acknowledgement message).

Third step (RECEIVE_ACK)

A receives the parameters of B (parametersOfB), retrieves the interaction these refer to and notifies its miner about the update:

import eu.h2020.helios_social.core.contextualegonetwork.Interaction;
import eu.h2020.helios_social.modules.socialgraphmining.SocialGraphMiner.InteractionType;

ArrayList<Interaction> edgeInteractions = A.contextualEgoNetwork
				.getOrAddEdge(A.contextualEgoNetwork.getEgo(), contextualEgoNetwork.getOrCreateNode(nameOfB, null))
Interaction interaction = interactions.get(interactions.size()-1);
miner.newInteraction(parametersOfB, InteractionType.RECEIVE_REPLY);

Diffusing predictions through the decentralized social graph

The mining module can be used to augment the predictive capabilities of other modules through graph diffusion. This is achieved through the PPRMiner, which implements a decentralized version of the random walk with restart scheme to aggregate predictions across ego network alters with the ego's device predictions. To use this miner, we consider a scheme where each device makes its own prediction about its users (e.g. a classification of their interests), where predictions are not necessarily made for all devices, for instance due to lack of data features. At worst, some device users could have manually classified themselves.

In this setting, we consider that each device encodes its user's information into a personalization vector, which is an one-hot encoding of their classification label. For example, given that the device's user is assigned to the class cl=0,1,...,n-1 out of n potential classes, this one-hot encoding can be obtained per personalization = new DenseTensor(n).put(cl,1); where DenseTensor is a data structure to manipulate vectors provided by the JGNN library. This expression creates a vector of n zeros, where a value of 1 is placed on position cl. For users with unknown classification labels, the personalization vector comprises only zeros and can be constructed per personalization = new DenseTensor(n);.

Then, in each device, the miner needs to be constructed given a unique name (that differentiates between multiple usages by different modules) the contextual ego network instance to attach information to and the personalization vector for the device's user per pprMiner = new PPRMiner(name, contextualEgoNetwork, personalization). The personalization can be updated later on through the pprMiner.updatePersonalization(personalization); method. To further prevent diffusion to change non-zero (i.e. known) personalizations, the PPRMiner can be made to forcefully keep known predictions through miner.setPersonalizationAsGroundTruth(true);

As a final note, PPRMiners extend the social graph mining class and hence need to exchange parameters with their neighbors given the previous communication scheme. For example usage of the PPRMiner, please refer to the simulation code at

Project Structure

This project contains the following components:

src - The source code files.

doc - Additional documentation files.

jar - Jar file installation.

License: Apache License 2.0