As data becomes increasingly interconnected and systems increasingly sophisticated, it’s essential to make use of the rich and evolving relationships within our data. Graphs are uniquely suited to this task because they are, very simply, a mathematical representation of a network. The objects that makeup graphs are called nodes (or vertices) and the links between them are called relationships (or edges).

[See more articles from ODSC West 2019 speakers here!]

A property graph model consists of entities, often called nodes, and links between them, often called relationships. Nodes and relationships can also contain properties and attributes.

Graph algorithms are built to operate on relationships and are exceptionally capable of finding structures and revealing patterns in connected data. This is important because real-world networks tend to form highly dense groups with structure and “lumpy” distributions. We see this behavior in everything from IT and social networks to economic and transportation systems. Traditional statistical approaches don’t fully utilize the topology of data itself and often “average out” distributions. Graph analytics vary from conventional analysis by calculating metrics based on the relationships between things.

Graph Algorithms

Graph algorithms are used when we need to understand structures and relationships to answer questions about the pathways that things might take, how they flow, who influences that flow, and how groups interact. This is essential for tasks like forecasting behavior, understanding dynamic groups, or finding predictive components and patterns.

There are many types of graph algorithms but the three classic categories consider the overall nature of the graph: pathfinding, centrality, and community detection. However, other graph algorithms such as similarity and link prediction consider and compare specific nodes.

• Pathfinding algorithms are fundamental to graph analytics and explore routes between nodes.
• Centrality algorithms help us understand the impact of individual nodes to the overall network. They identify the most influential nodes and help us understand group dynamics.
• Community algorithms find communities where members have more relationships within the group that outside it. This helps infer similar behavior or preferences, estimate resiliency and prepare data for other analyses.
• Similarity algorithms look at how alike individual nodes are by comparing the properties and attributes of nodes.
• Link Prediction algorithms consider the proximity of nodes as well as structural elements, such as potential triangles, to estimate the formation of new relationships or the existence of undocumented connections.

Example Combating Fraud

Let’s say we’re trying to combat fraud in online orders. We likely already have profile information or behavioral indicators that would flag fraudulent behavior. However, it can be difficult to differentiate between behaviors that indicate a minor offense, unusual activity, and a fraud ring. This can lead us into a lose-lose choice: Chase all suspicious orders—which is costly and slows business—or let most suspicious activity go by. Moreover, as criminal activity evolves, we could be blind to new patterns.

Graph algorithms, such as Louvain Modularity, can be used for more advanced community detection to find group interacting at different levels. For example, in a fraud scenario, we may want to correlate tightly knit groups of accounts with a certain threshold of returned products. Or perhaps we want to identify which accounts in each group have the most overall incoming transactions, including indirect paths, using the PageRank algorithm.

To illustrate these algorithms, below is a screenshot using Louvain and PageRank on season two of Game of Thrones. It finds community groups and the most influential characters using our experimental tool, the Graph Algorithms Playground. Notice how Jon is influential in a weakly-connected community but not overall, and that the Daenerys group is isolated. Interestingly, it’s been noted that highly connected “islands” of communities can signal fraud in certain financial networks.

Conclusion

We’ve quickly overviewed what graphs are and how graph algorithms are uniquely suited for today’s connected data, however, we’ve just scratched the surface of what’s possible.  If you’re interested in diving deeper, consider attending our training, “Reveal Predictive Patterns with Neo4j Graph Algorithms” at ODSC West 2019 on Wednesday, October 30th.

[Related Article: Creating Multiple Visualizations in a Single Python Notebook]

We also recommend downloading a free copy of the O’Reilly book, “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” while it’s still available.  This book walks through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j, including a chapter dedicated to machine learning.

Authors:

Jennifer Reif

Jennifer is a Developer Relations Engineer at Neo4j, conference speaker, blogger, and an avid developer and problem-solver. She has worked with a variety of commercial and open source tools and enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.

Amy E. Hodler

Amy is a network science devotee and a program director for AI and graph analytics at Neo4j. Amy is the co-author of Graph Algorithms: Practical Examples in Apache Spark and Neo4j. She tweets @amyhodler

Originally posted on OpenDataScience.com

Editor’s note: Abon is a speaker for ODSC West this Fall! Consider attending his talk, “Computer Vision for E-Commerce: Intelligent Analysis and Selection of Product Images at Scale” then.

In e-commerce, the role of product images is critical in delivering satisfactory customer experience. Images help online shoppers gain confidence in a product and increase their engagement with the product, increasing the likelihood of purchase. Hence, from the perspective of an e-commerce company like Walmart, images are an integral, vast, and valuable component of its catalog. We are motivated to analyze images for several reasons: to measure the quality of images, to detect and discard offensive pictures, to select and rank them by their relevance to a product.

[Related Article: 4 Examples of Businesses Solving Problems with AI]

Image analysis for an e-commerce catalog such as ours typically goes through the following stages (not necessarily in this order):
Filter images by content or quality – This covers several binary classification problems, each addressing a quality (such as sharpness) or a compliance issue (such as violent or adult content). Some of them, especially the compliance problems, are ill-posed with severe class imbalance. Some of them are better treated as object detection than classification.
Classify images based on content – This stage categorizes the images of a product into several buckets based on their viewpoint or other characteristics such as lifestyle vs. solo images, product image vs packaging image and so on.
Extract content from images – In this step, specific information such as textual attributes is extracted from images wherever possible. An example of this is detection of the drug facts table from the picture of medicine and extraction of the ingredients. Another example is intelligent extraction of a flat or a textured square region from apparel images that can be used as a thumbnail or swatch on the website.

For curious readers, here are the links to our recent papers that discuss our image analysis pipeline in more detail:
https://arxiv.org/abs/1811.07996
https://arxiv.org/abs/1905.02234

We deal with a number of challenges while building models or algorithms that are part of the above-mentioned pipeline. Let us focus on one of them in this article: shortage of training data. The challenge, far more commonplace than you think, arises primarily for two reasons:

1. The problem we want to detect manifests rarely, making it difficult to find examples of the “positive” class. A typical example of this would be an offensive image such as a racially inappropriate symbol on a hat. Usually, one such image is found and reported by a customer by accident. In reality, less than 0.01% of the products have such an image. However, there is no easy way to sieve through the catalog to find more examples.
2. The scale of the problem makes image annotation prohibitively expensive. Let us say that we want to classify images into five viewpoints – top, bottom, left, right, and close-up – for a product, and then scale it for 10,000 types of products. This means we have an extreme-scale classification problem with ~50,000 classes to solve. Since different viewpoints are often close to each other in color and shape, we do need a decent amount of annotated images for each class. Also, for such a fine-grained task, we should probably leverage a trained crowd, which is more expensive, as opposed to completely anonymous ones to ensure a better quality response. Even if we ask for 10 annotated images per class, the cost of annotating half-a-million images can be too high for many projects in many companies.

We often take recourse to a number of practical strategies – some conventional and some ad hoc or unique – to deal with this challenge.

1. Data augmentation: Standard techniques of image data augmentation include color and geometric space transformations. They often cannot produce enough useful data, hence problem-specific custom techniques such as superimposition or image synthesis are applied.
2. Few shot learning: When only a few examples of a class are known in advance, a mix of few shot learning and a conventional classifier often produces better results than one of them alone. For example, consider the picture of an “energy guide” of a television. The look of it remains almost the same regardless of the brand and model of the televisions. Hence, it is possible to build a classifier for this class with very few examples. However, the “close-up” views of the different models of a television vary so much that a few-shot learner will easily overfit.
3. Iterative training – When we do not have enough training data to build a high-precision model, we start with shallow linear classifiers, very small neural nets or heuristic-based classifiers. These baseline models, which often work as low precision and moderate recall predictors, are used to generate predictions. Depending on the crowdsourcing budget, a percentage of the high confidence predictions are sent for manual review. The reviewed images are fed back to the baseline classifiers, and they are retrained. This process is repeated until some base models are good enough, or we have enough data to develop a full-scale model.
4. Multi-stage inference – When it is expensive to procure training data for a complex task or it is compute-intensive to run a complex model on the entire set of products, we try to divide the problem into two. A precursor, namely a simpler image model or a non-image model that learns from contexts such as product title and category, is added before the main model. A typical example would be the detection of nudity – a problem more likely to occur in certain categories such as wall arts or books. Hence, adding a faster and lightweight classifier that separates books and wall arts from the rest of the catalog reduces the load on the slower and deeper object detection network that is trained to detect nudity. Also, the training data can be collected from those categories only instead of the entire catalog.
5. Transfer (and meta) learning – Last but not the least, appropriate use of transfer learning and meta-learning, when possible, can produce high-quality results with a relatively small amount of data. If the problem at hand involves classes similar to the ones available in public datasets such as Imagenet or Coco (for example, we want to detect rocking chairs that are similar to dining chairs); or the problem requires finer classification of such a  class (for example, we want to distinguish between left-facing and right-facing pictures of shoes), beginning from a pre-trained model and fine-tuning it is a great practical idea.

[Related Article: Deep-Text Analysis for Journalism]

Overall, developing classification and object recognition solutions for real problems and scaling them for an enormous catalog is an intriguingly complex problem. To understand more about how we learn from the textbooks and then deviate from them to solve these problems, consider attending my talk at ODSC West in San Francisco from October 29th-November 1st.

Originally posted on OpenDataScience.com

Bill is a speaker for ODSC West 2019 this November in San Francisco! Attend his talk “From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact” then!

If a tree falls in the forest… you know the aphorism. I’m using it here to remind you that your most important job is not data science – OK, maybe it’s not more important, but it’s equally important – is communications. When you do work, and your work remains in a vacuum, it serves no purpose. If your colleagues, bosses, clients don’t learn from your data analysis, then it is literally useless.

Did you know that one of the most-referenced skill requirements in job postings for data science professionals is communications? Yes, your potential employer knows how important it is too – it’s not just me saying this!

So communications is important – really important? Agree? Keep reading.

How do you communicate? Do you just show someone a spreadsheet and say nothing? Do you print out a chart, deliver it to your colleagues via inter-office mail (does that still exists?!?) without context? Do you email someone a giant list of facts in bullet point form? I hope not!

No, you tell a story. Story is defined in overly-complicated ways. Let me simplify. I just mean you tell a logical flowing linear sequence of information with continuity and completeness. There is a beginning, middle, and end. And your audience is not left doubting your credibility or thinking you’re hiding something or holding back when you’re done.

[Related article: Advantages and Best Uses of Four Popular Data Visualization Tools]

Here’s the thing – storytelling is an evolutionary imperative. Story is literally the most important thing that has helped humans survive and evolve as a species. As Lisa Cron says, in Wired for Story, “Story, as it turns out, was crucial to our evolution—more so than opposable thumbs. Opposable thumbs let us hang on; story told us what to hang on to.” It’s the story of the Neanderthal who ate the berries that poisoned him and led to a horrible, painful death that taught the entire village to avoid that one type of berry. Not everyone could witness it first-hand. And that story was told in a logical flowing way. It was not told like so: “Red berries. Death. Pain.” That might have worked…but a more dramatic, emotional, flowing story with memorable details would be far more effective.

For you doubters out there – for the data purists who insist “we are scientists – it’s different for us!” I point to this study, which found that the most influential scientists (those whose research is cited most often by other scientists) are those who use narrative techniques in their research reports.

Storytelling is critical, effective, and will help you do your job better. I’ll be talking about data storytelling at ODSC East in May and I can’t wait to share some tips and tricks, and research-driven best practices for storytelling and visualization that will help you think differently about your next project and implement specific changes to your work the moment you leave the room!

[Related article: 6 Reasons Why Data Science Projects Fail]

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Editor’s note: Be sure to attend his talk “From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact” at ODSC West later this year!