Graph Analytics

Graph Algorithms or Graph Analytics are analytic tools used to determine strength and direction of relationships between objects in a graph. The focus of graph analytics is on pairwise relationship between two objects at a time and structural characteristics of the graph as a whole. Typical terms and concepts in this context are centrality measures, clustering, reciprocity, spanning trees and more. Graph analytics can be part of graph visualization provided the data is small but one typically implements things in middleware in order to pre-process big-data using Spark, Databricks or similar stacks. There are many ready-to-go solutions on the market like Linkurious Enterprise and Cambridge Semantics but some problems (and domains like anti-terrorism and intelligence) require custom development and techniques.

We help companies approach challenging problems and have a broad experience in many industries. We have developed solutions involving natural language processing (bots e.g.), PhD-level techniques to optimize digital marketing, big-data graph mining solutions towards fraud detection, custom graph machine learning models and more. Technology is for us a tool and we are in this sense technology-agnostic, whether your preferred stack is .Net, Python, Java or Rust, we focus on finding a business solution.

As an independent consulting company we help you find a path through the technology jungle. While the well-known consulting firms will advice you using off-the-shelf enterprise packages, we provide an objective view and long-term solution within the scope and budget of your business.

Our Services

Forensic Analysis

We take a holistic approach to forensic analysis, combining the big picture with meticulous attention to detail. Using advanced visualizations and data analytics techniques we extract meaningful stories from large datasets, transform evidence into conclusive proofs, and reveal facts that can be used in legal cases.

Data Storytelling: We bring complex data sets to life by identifying patterns, trends, and connections that inform decisions.
Evidence Analysis: We rigorously examine data to create airtight arguments and proofs for use in court proceedings.
Relational Data Integration: By combining temporal and spatial data with relational analysis, we uncover hidden facts that can be used to build compelling cases.

Fraud detection

We specialize in crafting algorithms to uncover fraud rings, anomalies and unusual behavior patterns. By merging time series analysis with machine learning techniques applied to networks and/or geospatial datasets, we transform data into actionable insights.

Identifying Money Laundering Schemes: applying graph ML to analyze financial transactions.
Uncovering Identity Theft Rings: geospatial data analysis combined with graph analytics to identify suspicious login activity from multiple locations.

Crime Prevention

To grasp the underlying narrative of criminal cases, it’s essential to explore the story that is unfolding, identify the key players involved, understand the context in which events transpire, and analyze the methods employed by criminals. By leveraging advanced graph machine learning techniques, we develop inference engines that predict potential criminal activities based on emerging patterns and trends.

Homicide Investigation: analyzing the narrative, including the victim’s background, relationships with potential suspects, and any previous conflicts. Crime ontology and visualization.
Fraudulent Scheme: visualizations of Ponzi or phishing scams, applications of graph algorithms and more.
Cybercrime Incident: extracting actor, victims (companies or individuals) from networks and time series. Predicting events and the usage of knowledge graphs to generate the full story behind anomalies.

Epidemics

We view the spread of information as a dynamic process across interconnected networks and domains. The methodologies we employ to analyze these flows extend far beyond just biological contexts; they can be applied to various phenomena such as the dissemination of misinformation, software vulnerabilities, trends in consumer behavior, systemic failures in infrastructure, and interactions among diverse species.

Misinformation Tracking: we help media companies and social platforms identify and analyze the spread of fake news across their networks. By visualizing information pathways, we enable organizations to understand how false narratives propagate and develop strategies to mitigate their impact.
Cybersecurity Threat Analysis: advanced graph analysis techniques to detect and visualize the spread of computer viruses or malware within digital ecosystems. By mapping out the connections between infected systems, we empower IT departments to respond proactively and strengthen their defenses against potential breaches.
Consumer Trend Forecasting: retailers can benefit from our expertise in examining how fashion trends or product preferences emerge and evolve within social networks. By identifying key influencers and patterns in consumer behavior, we provide actionable insights that guide marketing strategies and inventory management.

Ontologies

In many industries, the concepts of graph schemas and ontologies remain underappreciated. Surprisingly, numerous NoSQL and graph solutions overlook these essential components entirely. We recognize the importance of a robust long-term knowledge management solution. We help you create tailored ontologies that fit your unique needs and guide you through the complexities involved in developing a knowledge base that you can leverage effectively.

Data Integration: integrating diverse data sources and design an ontology that standardizes terminologies and relationships between various entities. This structured knowledge base enables better data interoperability and enhances clinical decision-making.
Enhanced RAG: generating more accurate insights based on a deeper understanding of vectors/data via ontologies.
RDF Guidance: we help you navigate the RDF jungle and graph databases to find a solution which suits your vision and technology.

Knowledge Graphs

Our consulting services help you effectively organize domain-specific data within a cohesive framework provided by a knowledge graph. By establishing clear ontologies tailored to your industry—be it finance, healthcare, or technology—we empower you to query interconnected datasets seamlessly. This capability allows decision-makers to gain insights quickly without sifting through disparate data sources. Integrating knowledge graphs into your AI initiatives significantly enhances their performance by providing rich contextual information that improves understanding and reasoning capabilities.

Natural Language Processing & Knowledge Graphs: NLP and AI techniques to extract meaningful entities and relationships from unstructured text. Generate language from knowledge graphs.
Graph database: we help you define a property graph or triple store and specialize in Neo4j, Amazon Neptune and Memgraph as backends for advanced visualizations.

Marketing Optimization

Graphs are the secret ingredient in optimizing your digital marketing strategies. Whether you’re identifying key pathways to conversion, analyzing transitions between customer touchpoints, or modeling complex behaviors through Markov chains, we can provide the insights you need.

Customer Journey Mapping: we utilized graph analytics for major clients (Bank of America, Coca-Cola, Nike…) to visualize customers’ journeys. By mapping out touchpoints and interactions, we identified critical paths that significantly increased conversion rates by 25%.
Predictive Modeling: we help leading telecommunications company (Vodafone, Orange…) by applying Markov chain models and portfolio optimization to predict customer churn. This allowed them to proactively engage at-risk customers, resulting in a 15% reduction in churn rates.
Campaign Optimization: by leveraging graph analytics, we pinpointed the most effective channels and touchpoints.

Social Network Analysis

At its core, social network data has emerged as a crucial complement to internal marketing insights, providing unparalleled visibility into customer behaviors and relationships. Our expertise lies in crafting complex networks that reflect the nuances of your business ecosystem, enabling you to unlock hidden value through graph analytics and informed decision-making.

Identifying Influencers: the relationships between users and identify individuals with high centrality measures (e.g., degree, closeness, or betweenness). These users are likely to be influential in spreading information throughout the network.
Detecting Communities: identify clusters of users who share similar interests and engage with each other frequently. These clusters represent the communities within the social network.
Tracking Information Diffusion: track the propagation of information through the network, identifying key nodes (e.g., influencers) and edges that contribute to its diffusion.

Natural Language Understanding

Smart decision-making and artificial intelligence are increasingly intertwined with Natural Language Processing (NLP). Our focus is on assisting companies in leveraging deep learning techniques to analyze and interpret text and speech effectively.

We specialize in transforming language into graph structures, which enables semantic labeling, enhances question-answering capabilities, and facilitates a deeper understanding of context. By harnessing the power of language, we help organizations augment their existing data, driving insights and fostering innovation.

Whether it’s through improving customer interaction systems or extracting meaningful information from vast datasets, our approach to NLP empowers businesses to make informed decisions based on nuanced language understanding.