Hadoop MapReduce over Hive based implementation of attributed network pattern matching.


This code performs multi-attributed network pattern matching between a model (or query) graph and a data graph. We generally expect that model graphs are small (~10-100 nodes) while data graphs can be very, very large (millions of nodes, tens-hundreds of millions of edges). We represent each network as multi-attributed graph G=(VG, EG, AG) consisting of nodes v ∈ VG, links (v, w) ∈ EG, and their attributes aGvw ∈ AG. Node attributes aGvv define profiles of activities, roles, actors, and geographic nodes while link attributes aGvw define profiles of relations among those entities, encoding semantic relations, temporal and spatial dependencies, interactions, and influence among different nodes. Values of individual attributes in our representation can be single numbers, ranges, and strings. Note that throughout the code and documentation, the words relation, edge, and link are used interchangeably.


Figure 1: (a) Model network and (b) Data Network going through (c) a multi-attributed matching process

Data networks (Figure 1b) represent observations about specific entities and their relations in a preloaded dataset. Model networks (Figure 1a) represent queries, hypotheses or inferences concerning node-link structures of interest to the analyst. For example, data nodes can include actors (specific individuals or groups) and geographic areas (locations and facilities), while model nodes can include tasks (short-term activities performed by actors at geographic areas; e.g., weapons storage, reconnaissance) and roles (conceptual abstractions of behaviors, intents, and activities associated with actors or areas).

Mismatches between model and data networks are computed by comparing the attributes of the model node/links with the attributes of the various data nodes/links iteratively (Figure 1c). The belief propagation algorithm is used to encode these mismatches within two types of messages: 1) Node messages that encode the likelihood of a certain model node matching with a certain data node, 2) Link messages that encode the likelihood of a certain model link matching with a certain data link. This process is iterated through and messages are continuously updated until convergence. Convergence occurs when model nodes/links have minimized their mismatches and the messages no longer pull an element to another place within the data. Note that this does not limit the matches to a single match within the data graph

While some details of this code are tied to the execution framework (Hive), the processing technique used (MapReduce) and the datasets processed (Bitcoin, Akamai datasets), the core of the code is general and designed to be reusable. The MapReduce implementation was utilized to test the performance of the belief propagation algorithm in a parallel fashion. Its use was motivated by having the data graph split up into multiple smaller networks, and computing mismatches between model and data graphical elements in parallel, but the iterative process required by loopy belief propagation (Levchuk, Roberts, Freeman; 2014) has shown that MapReduce is not the best of tools for supporting multi-attributed network pattern matching.

We are constantly exploring new processing techniques and data frameworks (e.g. BSP w/ Giraph, Spark/GraphX, Titan/Blueprints) and look forward to updating this repository with our findings.

Any query or dataset that can be modeled as an 'attributed network' - nodes and edges with arbitrary attributes, can be processed using these techniques.

Algorithm and Processing Techniques


First, we run a map() function over the Hive input columns of the datasets to extract a mismatch value for the data nodes and edges against the model nodes and edges. This value encodes how well of an individual match each pairing represents. Node mismatches look at things like in/out degrees as well as attributes. Edge mismatches primarily look at attributes. This part of the process scales well with the number of mappers available.


Next, we distribute these mismatch values to our reducers via Hive's DISTRIBUTE and SORT BY operators . For Bitcoin and Akamai we use a time window binning technique. This can be varied depending on the desired query (e.g. X days, weeks, months years). All of the data that falls into the same bin will be processed by that reducer. Other values can be emitted by the map step and used instead of time if desired, for example a clustering/subgraph categorization/other techniques).


The reducers collect all of the relevant mismatch functions for the nodes and edges sent to their bin. Next, a belief propagation algorithm is run to allow the nodes and edges to exchange information with their neighbors, update their own value, and repeat. This process is similar algorithmically to PageRank and other message passing algorithms. Iteration continues until we converge, after which we can sample the mismatch space to find data nodes that match model nodes with high relevance. This produces the desired result - a mapping for every model node to 1 or more data nodes. By repeating the sampling process we can produce additional results and permutations from the data graph.



To build, run the command:

mvn clean package 


This release targets the ability to run over Hive datasets from XDATA. The two datasets currently supported include the Akamai data and Bitcoin data. Future releases are focusing on documentation and extensibility to support additional datasets by allowing flexible column/dataset specification, updated mismatch functions, and pattern authoring.


After building run against either dataset by using the appropriate Hive script. Note these currently require access to a Hive system running the X DATA datasets "a_s_test", "bitcoin_inout_edges", or the equivalent. See below on Datasets for additional information.

hive -f akamaiDemo.hql
hive -f bitcoinDemo.hql

Output Format

Results are written to an output Hive table: aptima_pm_result.

The result format for node matches looks like:

result_num modelID dataID mismatch dir
Unique result number, one for each per match ID of model node in query ID of matching data node mismatch value --

Mismatch values of a 0.0 represent a perfect match between the model node and data node.

dir is a special case for Bitcoin and used to denote edge values. This optional information is included to help analyze and verify the results. This additional information is encoded as:

result_num modelID dataID mismatch dir
Unique result number, one for each match ID of model node in query Same as model node Edge value IN or OUT

Where IN and OUT represent values seen coming IN or OUT of a given model node. For Bitcoin, this is the Bitcoins transferred on the link.

Example Patterns

The patterns encoded for the Akamai and Bitcoin demos initially look at a similar attributed graph construct that occurs in both datasets:


For Bitcoin this could represent an instance of a source node distributing Bitcoins to a series of intermediate nodes then recollecting them at a 'sink' node.

For Akamai this could represent a redundant router configuration where a node is load balancing between several intermediate routers on the way to a destination.


Additional documentation on the input data tables:


Akamai uses a table format like:

src dest region unixtime latency
Source IP Destination IP Region # Timestamp Latency(ms)


Bitcoin uses a table format like:

id source_edge_ID destination_edge_ID dtg amount in_degree out_degree node incoming_amount outgoing_amount
Transaction ID Source Node ID Destination Node ID Date time Bitcoin amount In degree Out degree Node ID Incoming Bitcoin sum Outgoing Bitcoin sum

Where if the row is an edge, source_edge_ID and destination_edge_ID, and amount will be populated and node, in degree, out degree, incoming sum, and outoing sum will be null. If the row is a node, this will be reversed. Date/Time is populated for both types.


Feel free to reach out to us at [email protected]