  Alarm root cause analysis is a significant component in the day-to-day
telecommunication network maintenance, and it is critical for efficient and
accurate fault localization and failure recovery. In practice, accurate and
self-adjustable alarm root cause analysis is a great challenge due to network
complexity and vast amounts of alarms. A popular approach for failure root
cause identification is to construct a graph with approximate edges, commonly
based on either event co-occurrences or conditional independence tests.
However, considerable expert knowledge is typically required for edge pruning.
We propose a novel data-driven framework for root cause alarm localization,
combining both causal inference and network embedding techniques. In this
framework, we design a hybrid causal graph learning method (HPCI), which
combines Hawkes Process with Conditional Independence tests, as well as propose
a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge
weights. We subsequently discover root cause alarms in a real-time data stream
by applying an influence maximization algorithm on the weighted graph. We
evaluate our method on artificial data and real-world telecom data, showing a
significant improvement over the best baselines.
