Big Data - 5

Mining Social Network Graphs

What is social network graph mining

Social network graph mining involves analyzing the structure of a social network to discover useful patterns and information. It typically involves:

• Representing the social network as a graph

• Analyzing the graph structure using techniques like:

  • Clustering to find densely connected groups

  • Centrality measures to identify important nodes

  • Pathfinding to determine the shortest paths between nodes

  • Recommendation systems based on network structure and node attributes

• Visualizing the graph to gain insights

The goal is to gain useful insights from the network structure that can help with applications like:

• Recommending friends or connections

• Detecting communities

• Finding influential users

• Predicting the spread of information

Applications of social network analysis

Some applications of social network graph mining include:

• Recommendation systems - Recommending friends, groups to join, pages to follow based on your network

• Viral marketing and influence analysis - Identifying influential users who can help spread information or products

• Detecting communities and subgroups - Helping users find others with similar interests

• Fraud detection - Identifying suspicious patterns in transaction or communication networks

• Job recommendations - Recommending jobs to users based on their network of connections

• Predicting the spread of information - Understanding how information and ideas spread through the network

• Expertise location - Identifying experts in particular domains based on their connections

Social Networks as a Graph

Nodes represent actors

In a social network graph, nodes typically represent:

• Individual people

• Groups

• Organizations

• Pages

• Any entity that has relationships with other entities in the network

Edges represent relationships

Edges between nodes represent some type of relationship between the actors. For example:

• Friendship

• Collaboration

• Membership

• Following

• Communication

Edges can be either directed or undirected depending on the type of relationship.

Weighted and directed graphs

Social network graphs are often:

• Weighted - Edges have weights that represent the strength or frequency of the relationship.

• Directed - Relationships have a direction. For example, followers on Twitter form a directed relationship.

For example:

• An edge from A to B with weight 5 represents that A follows B 5 times.

• An edge from A to B without a weight simply indicates that A follows B.

Types of Social Networks

There are many different types of social networks based on their purpose and relationships:

Social networking sites

These are general-purpose social networks like:

• Facebook - Based on friend connections

• Twitter - Based on follower connections

• LinkedIn - Based on professional connections

They allow people to connect, and share updates and media. Relationships are often undirected (friends) but can be directed (followers).

Collaboration networks

These networks are based on collaboration between people. Examples are:

• Co-authorship networks - Nodes are authors, and edges represent co-authored papers.

• GitHub - Nodes are developers, and edges represent collaboration on projects.

These networks typically have weighted edges to represent the strength or frequency of collaboration.

Information networks

These networks are based on the sharing or consumption of information. Examples are:

• Citation networks - Nodes are research papers, and edges represent citations.

• Reddit - Nodes are users, and edges represent replies/comments between users on posts.

They often form directed graphs since information/content flows in a particular direction.

Other types of social networks:

• Communication networks - Based on calls, messages between people

• Affiliation networks - Based on membership in organizations

• Transaction networks - Based on financial transactions between entities.

Clustering of Social Graphs

Clustering social network graphs aims to detect densely connected groups of nodes, also known as communities. This can help reveal the underlying group structure in the network.

Some common algorithms for community detection in graphs are:

Community detection algorithms

• Modularity optimization - Finds communities by maximizing the modularity score of the partition.

• K-clique percolation - Finds communities as the union of all k-cliques that can be reached through adjacent k-cliques.

• Label propagation - Starts with each node in its community and then nodes adopt the label that most of their neighbors have.

• Spectral clustering - Transforms the graph into a lower dimension and then applies clustering in that space.

• Clique percolation - Finds communities as the union of all cliques that can be reached through adjacent cliques.

These algorithms detect non-overlapping communities, where each node belongs to one community.

Overlapping and Hierarchical Communities

However, in many networks:

• Nodes can belong to multiple communities (overlapping communities).

• Communities can exist at different levels in a hierarchy.

For example, in a collaboration network:

• A researcher can belong to multiple research groups.

• Research groups can form departments, departments can form institutions, etc.

So more advanced algorithms can detect:

• Overlapping communities - Where a node can belong to multiple communities.

• Hierarchical communities - Communities at different levels, forming a dendrogram.

Direct Discovery of Communities

There are several algorithms for directly discovering communities in a social graph without any prior information:

Centrality measures

Centrality measures like degree centrality, betweenness centrality and closeness centrality can help identify important nodes that are likely in the centre of communities.

These central nodes can then be used as seeds to grow communities.

For example:

• Nodes with a high degree of centrality are likely hubs within a community.

• Nodes with high betweenness centrality act as bridges between communities.

Clique percolation method

This method looks for fully connected subgraphs called k-cliques in the graph.

Then it forms communities by:

1. Finding all k-cliques

2. Joining two k-cliques that share k-1 nodes

3. Repeating this process to grow communities

This forms non-overlapping communities.

Label propagation

This is a simple and efficient algorithm. It works as follows:

1. Assign each node a unique label initially

2. Each node adopts the label that most of its neighbors have

3. Repeat step 2 until stability (nodes keep their label)

Nodes with the same label at the end form a community.

This forms non-overlapping communities.

Recommender Systems

Recommender systems are software tools and techniques that provide suggestions for items that a user may like. They have become ubiquitous in applications like e-commerce, media streaming, news etc.

There are three main approaches to building recommender systems:

Content-based recommendations

These recommend items similar to what a user has liked in the past. They work as follows:

1. Create profiles of items based on attributes (genre, author etc)

2. Create profiles for users based on items they have liked

3. Match new items to users based on similarity between item and user profiles

Advantages:

• New users can be recommended items

• Recommendations are explainable

Disadvantages:

• Limited to item attributes

• Does not discover new correlations

Collaborative filtering

These recommend items that people with similar tastes and preferences have liked in the past. They work as follows:

1. Collect user preferences (ratings, purchases etc)

2. Find similar users based on item preferences

3. Recommend items that similar users have liked and the current user has not seen yet

Advantages:

• High accuracy since based on real user preferences

• Discovers new correlations not evident from item attributes

Disadvantages:

• Cannot recommend to new users

• Requires a large amount of user preference data

Hybrid recommender systems

These combine the content-based and collaborative filtering approaches to get the best of both worlds. They:

• Use content-based recommendations to bootstrap recommendations for new users

• Then incorporate collaborative filtering as more user data becomes available

This hybrid approach can overcome some limitations of each technique.

Building Recommender Systems

The process of building a recommender system typically involves the following steps:

Data collection and preprocessing

The first step is to collect the relevant data. This includes:

• Item data: attributes, descriptions, categories, etc.

• User data: profiles, preferences, demographic info, etc.

• Interaction data: ratings, purchases, likes, follows, etc.

This raw data then needs to be preprocessed:

• Clean missing or invalid values

• Encode categorical attributes

• Normalize numeric attributes

• Split into training and test sets

Model training

After the data is ready, a recommender model is trained on the training set. This involves:

• Choosing a recommendation approach: content-based, collaborative filtering or hybrid

• Selecting a suitable algorithm: kNN, matrix factorization, neural networks, etc.

• Training the model by finding patterns in the user-item interactions.

This results in a trained model that can make recommendations.

Making recommendations

Once the model is trained, it can be used to:

• Generate a ranked list of recommendations for each user

• Compute recommendation scores for unseen items

• Provide real-time recommendations as users interact with the system

The recommendations can then be evaluated using various metrics on the held-out test set.

Challenges in Social Network Analysis

There are several key challenges in analyzing social networks and building applications based on social data:

Data sparsity

This refers to the fact that users typically interact with or rate only a small subset of all available items. This makes it difficult to find meaningful patterns and correlations in the data.

Data sparsity can be addressed by:

• Collecting more user interaction data over time

• Incorporating side information like item attributes, user profiles, etc.

Cold start problem

This refers to the difficulty of recommending items to new users or items that have no interaction data. Since there are no preferences to base recommendations on, cold start is a major challenge.

It can be addressed by:

• Using content-based recommendations initially

• Bootstrapping from similar users' preferences

• Incorporating implicit feedback when explicit feedback is not available

Privacy and security

Social network data contains sensitive information about users, their connections and activities. This raises privacy and security concerns.

Some issues include:

• Unauthorized access to data

• Identifying users based on anonymized data

• Inferring private attributes from public data

• Misuse of recommendations for manipulation

Solutions require:

• Secure data storage and access control

• Anonymization techniques

• Privacy-preserving algorithms

• Transparency about data use and clear policies

Applications of Social Network Analysis

Social network analysis has many useful applications in business and research:

Customer segmentation

By analyzing the connections and interactions between customers in a social network, businesses can gain insights into natural customer groups or segments. This allows them to:

• Tailor products and services to specific segments

• Target marketing campaigns more effectively

• Improve customer experience and loyalty

Fraud detection

By looking for suspicious patterns in social networks, fraud and criminal activity can be detected. For example:

• Identifying rings of accounts engaged in fraudulent activities

• Detecting money laundering networks

• Uncovering identity theft syndicates

Market research

By studying social networks, businesses can gain valuable insights into:

• Emerging trends and popular topics

• Customer opinions and sentiments about products/brands

• The diffusion of new products and ideas through the network

This allows them to innovate and adapt to the market more quickly.

Some other applications of social network analysis include:

• Measuring influence and recommending influencers

• Improving team performance through network analysis

• Detecting the spread of misinformation and rumors

• Mapping the spread of diseases through communities