[I] Add the Betweenness Centrality graph algorithm [geaflow]

["Leomrlin (via GitHub)" <gi...@apache.org>]

Leomrlin opened a new issue, #801:
URL: https://github.com/apache/geaflow/issues/801

   ### What & why
   
   GeaFlow has PageRank and ClosenessCentrality, but not **Betweenness Centrality** —
   a measure of how often a vertex sits on shortest paths between other vertices,
   i.e. how much of a "bridge" it is. It's a core metric for social-network and
   key-node analysis. It's also genuinely hard: the classic Brandes algorithm has
   two phases (shortest-path counting + dependency accumulation), which makes it a
   satisfying high-difficulty challenge.
   
   GeaFlow 有 PageRank、ClosenessCentrality,但缺**介数中心性**(Betweenness)——衡量一个
   顶点作为"最短路径桥梁"的重要性,是社交网络/关键节点分析的核心指标。它实现难度大
   (经典 Brandes 算法需要两阶段:最短路径计数 + 依赖累积),非常适合作为高难度挑战题。
   
   ### The task
   
   Implement Betweenness Centrality on GeaFlow's vertex-centric / message-passing
   model (an unweighted-graph version is a fine first cut).
   
   基于 GeaFlow 的顶点中心/消息传递模型实现介数中心性(可先做无权图版本)。
   
   ### Where to look / 怎么做
   
   1. Reference the more complex existing algorithms:
      `udf/graph/PageRank.java` (multi-iteration + convergence) and
      `SingleSourceShortestPath.java` (shortest paths).
   2. Implement `AlgorithmUserFunction` using multi-round iteration + `sendMessage`
      for shortest-path counting and dependency back-propagation (the Brandes approach).
   3. Register in `BuildInSqlFunctionTable.java`.
   4. Use a small graph (5–7 nodes), compute the standard answer by hand, and turn
      it into `.sql`/`.txt` tests.
   
   ### Done when
   
   - [ ] Implemented and registered; `CALL betweenness() YIELD (id, score)` works
   - [ ] Results match hand-calculation / NetworkX on a small graph (attach the comparison in the PR)
   - [ ] Complexity and suitable scale documented (note it's for small/medium graphs if so)
   - [ ] Tests pass
   


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